Interview Question Anticipator

---
name: interview-question-anticipator
description: |
  Analyzes a job description and company profile to generate the 10-15 most
  likely interview questions the user will face, categorized by type (behavioral,
  technical, situational, role-specific) with guidance on what each question is
  really testing. Produces a prioritized question list with preparation notes.
  Use when the user wants to predict interview questions, prepare for a specific
  interview, or understand what interviewers are looking for. Do NOT use for
  writing STAR answers (use behavioral-interview-prep), technical problem-solving
  frameworks (use technical-interview-prep), or company research
  (use company-research-guide).
license: Apache-2.0
metadata:
  author: foundry-skills
  version: "1.0.0"
  tags: "interview-prep career analysis"
  category: "career-development"
  subcategory: "interview-preparation"
  depends: ""
  disclaimer: "none"
  difficulty: "intermediate"
---
# Interview Question Anticipator

## When to Use

**Use this skill when:**
- The user has a scheduled interview (any stage) and wants a prioritized prediction of the specific questions they are likely to face, mapped to the actual job description
- The user asks "what will they ask me," "what should I prepare for," or "what questions should I expect" before an interview
- The user has a job description and wants to understand which requirements will generate questions, in what order, and what the interviewer is actually evaluating with each one
- The user wants to identify the hidden subtext of likely questions -- what the interviewer is really testing versus what the question appears to be about
- The user wants to anticipate gap questions -- places where their background does not match the JD and an interviewer will probe
- The user is preparing for a specific interviewer type (HR screener, hiring manager, peer panel, skip-level executive) and wants to calibrate their preparation to that audience
- The user is switching industries or functions and needs to predict how their non-traditional background will be questioned
- The user wants to prepare smart questions to ask the interviewer that demonstrate genuine preparation

**Do NOT use this skill when:**
- The user wants to write full STAR-format behavioral answers with setup, action, and result -- use `behavioral-interview-prep` instead
- The user wants to practice live-coding problems, system design diagrams, or algorithm walkthroughs -- use `technical-interview-prep` instead
- The user is preparing for a case interview with market sizing, profitability frameworks, or M&A analysis -- use `case-interview-prep` instead
- The user wants to research the company's financials, culture, competitors, or news history -- use `company-research-guide` instead
- The user wants salary negotiation scripts -- use `salary-negotiation-prep` instead
- The user has already received questions and wants help answering them -- this skill predicts questions, it does not write answers
- The user wants general career advice about whether to take the job -- this skill is exclusively for interview preparation

---

## Process

### Step 1: Gather Interview Context Before Generating Anything

Do not begin generating questions without collecting sufficient input. Missing context produces generic output that fails the user. Ask the user to provide:

- **The full job description text** -- copy-pasted, not summarized. The exact wording of requirements is the raw material for question generation. Paraphrases lose the signal.
- **Company name, size, and stage** -- seed-stage startup (under 25 employees), growth-stage startup (Series A-C, 25-500 employees), scale-up (Series D+, 500-2,000), mid-market (2,000-10,000), or enterprise (10,000+). Stage determines interview formality, question structure, and what "culture fit" means.
- **Interview stage and format** -- initial recruiter screen (20-30 min), hiring manager first round (45-60 min), technical/skills round, team or peer panel, final round with leadership, or offer-stage culture conversation. Each stage has a distinct question profile.
- **Interviewer identity, title, and function** -- an HR recruiter, a direct hiring manager, a peer team member, a cross-functional stakeholder, or a C-suite executive each evaluate different competencies and ask structurally different questions.
- **Known interview format signals** -- has the company indicated "behavioral interview," "case-style discussion," "portfolio review," or "technical screen"? Any signal about format changes the question distribution.
- **The user's relevant background** -- a 2-3 sentence summary of their experience level, current role, industry, and any obvious gaps relative to the JD. The gap analysis is the most high-value output of this skill.

If the user provides only a role title and company name with no JD, proceed using industry and role-level norms, but clearly flag that predictions are probabilistic estimates, not JD-anchored predictions, and urge them to get the JD before the interview.

---

### Step 2: Systematically Decode the Job Description for Question Triggers

A job description is not a list of requirements -- it is an encoded set of signals about the organization's anxieties, priorities, and past failures. Each phrase signals a question. Parse the JD using this taxonomy:

**Hard requirement signals** (generates a direct competency probe):
- Any listed technology, tool, methodology, or framework generates a direct technical or competency question ("Tell me about your experience with X")
- Required years of experience in a specific area signals a scoping question ("Walk me through how you've used X over the last N years")
- Certifications or credentials generate a validation question ("How have you applied your [certification] in practice?")

**Soft requirement signals** (generates a behavioral question):
- "Fast-paced environment" → "Tell me about a time you managed competing priorities under a tight deadline"
- "Ambiguous or undefined problem space" → "Describe a project where the requirements were unclear. How did you move forward?"
- "Build from scratch / greenfield" → "Walk me through how you have built a [team/process/system] that did not exist before"
- "Cross-functional collaboration" → "Tell me about a time you had to influence an outcome without direct authority"
- "Data-driven" → "Give me an example of a decision you made using data. What data, what decision, what outcome?"
- "Customer-obsessed / customer-facing" → "Tell me about a time you advocated for a customer need that conflicted with an internal goal"
- "High-growth / scaling" → "How have you adapted your approach as your team or organization scaled?"

**Role responsibility signals** (generates situational questions):
- Ownership language ("Own the roadmap," "Be the DRI," "Lead the initiative") generates "What would you do in your first 90 days?" and prioritization questions
- Budget responsibility generates "Tell me about the largest budget you have managed and how you made trade-off decisions"
- Team management language generates "Tell me about your management style" and "Describe how you have handled underperformance"
- Executive visibility language generates "Tell me about a time you presented to senior leadership"

**Ordering matters:** Requirements listed in the first 40% of the JD are higher priority than those in the last 40%. Interviewers generally cannot probe all 15-20 requirements in a 45-minute interview, so they weight toward the opening items. Flag the top 5-7 requirements as the highest question probability.

**Absence signals:** What the JD does NOT mention is also informative. A PM JD that does not mention A/B testing is less likely to probe it deeply. A JD that mentions "strong communication" without specifying written vs. verbal or executive vs. technical communication is using a generic filler phrase -- do not over-index on it.

---

### Step 3: Apply Interview Stage and Interviewer-Type Filters

The same role generates different questions depending on who is in the room and what stage of the funnel you are in. Apply these filters after generating the raw question set:

**Recruiter / HR screen (20-30 minutes):**
- Questions focus on: compensation alignment, availability, location/remote preferences, basic qualification verification, culture fit signals, and "tell me about yourself" structured narrative
- The recruiter almost never evaluates deep technical competency -- they are screening for disqualifiers
- High-probability questions: "Walk me through your background," "Why are you looking to leave?", "What are your compensation expectations?", "Why this company / this role?", "What is your availability?"
- The recruiter often reads from a structured scorecard -- there is a predictable question set

**Hiring manager first round (45-60 minutes):**
- Questions focus on: competency depth, specific examples from the resume, role understanding, team fit, and early leadership signal
- The hiring manager is evaluating whether they can work with you and whether you can do the job
- High-probability questions: deep behavioral dives (STAR probes), situational scenarios based on real team challenges, vision alignment ("How would you approach X problem we have?"), and direct technical validation

**Peer / team panel (60-90 minutes, multiple interviewers):**
- Each panelist typically owns a question domain: one asks technical questions, one asks behavioral questions, one asks cross-functional or collaboration questions
- Questions are less standardized because different team members have different priorities
- Expect overlap -- more than one interviewer will ask a variation of "tell me about a challenging project"
- Expect the technical panelist to probe deeper than the hiring manager would

**Skip-level / executive final round (30-45 minutes):**
- Questions focus on: strategic thinking, values alignment, vision, and how the candidate handles ambiguity and pressure
- Executives rarely ask granular technical questions; they ask about judgment, leadership philosophy, and organizational impact
- High-probability questions: "What is your leadership philosophy?", "Tell me about a time you had to make a difficult decision with incomplete information", "Where do you see this function/team/company in 3-5 years?", "What would you do differently if you could go back?"
- Executives are also assessing executive presence -- composure, conciseness, and confidence under pressure

---

### Step 4: Generate and Categorize the Question Set

Produce 10-15 questions distributed across four categories. Do not exceed 15 -- beyond that, the list becomes noise that dilutes preparation focus.

**Behavioral questions (4-5 questions):**
- Each behavioral question maps to a specific competency (leadership, conflict resolution, failure handling, prioritization, cross-functional influence, bias toward action, etc.)
- The question must be anchored to a specific JD phrase or implied competency from the role
- Use the "Tell me about a time" / "Describe a situation where" construction -- these are the standard behavioral prompts
- Identify the competency the question is screening for, not just the topic (e.g., "Tell me about a time you failed" is screening for self-awareness and learning orientation, not for failure history)

**Technical / domain questions (3-4 questions):**
- Calibrate depth to the stated experience level: entry-level gets definitional questions ("What is X and when do you use it?"), mid-level gets application questions ("How have you used X in a project?"), senior-level gets architectural or strategic questions ("How would you evaluate whether X is the right tool for this problem?")
- Each technical question maps to a specific listed tool, methodology, or required skill in the JD
- For non-engineering roles, "technical" means domain-specific knowledge (e.g., for a marketing role: campaign attribution methodology, CAC/LTV modeling, funnel analysis; for a finance role: DCF construction, variance analysis, covenant compliance)

**Situational / hypothetical questions (2-3 questions):**
- These begin with "What would you do if...," "Imagine you are...," or "How would you approach..."
- They are derived from the real challenges implied by the role (hiring language: "you'll be building this from scratch" → "What would you do in your first 30 days to understand the current state?")
- Situational questions test planning and reasoning ability rather than past performance
- Senior roles receive more situational questions; junior roles receive more behavioral questions

**Role-specific / fit questions (2-3 questions):**
- "Why this company?", "Why this role?", "Why now?" -- motivational alignment
- "Where do you see yourself in 3 years?" -- retention risk and ambition calibration
- "What is your ideal working environment?" -- culture fit signal
- "What are your greatest strengths / weaknesses?" -- self-awareness and honest self-assessment
- These are almost always asked by the hiring manager and are highest likelihood despite appearing generic

---

### Step 5: Decode the Hidden Subtext of Each Question

For every question in the forecast, go beyond the surface topic. Provide:

**The actual competency being evaluated:** Every interview question is a proxy for a real job requirement. "Tell me about a time you disagreed with your manager" is not about conflict -- it is about psychological safety, intellectual courage, and whether the candidate can influence upward without damaging relationships.

**What a strong answer includes:** Identify the 2-3 specific elements that differentiate a good answer from a great one. For behavioral questions, a strong answer includes a specific metric or outcome, evidence of deliberate thinking (not just what happened but why they chose that approach), and a learning or adaptation signal. For technical questions, a strong answer demonstrates nuance -- knowing when NOT to use a tool, not just when to use it.

**What a weak answer looks like:** Name the specific failure modes. For "Why are you leaving your current job?" the weak answer names specific problems with current colleagues or management (signals poor judgment and a potential flight risk). For "Tell me about your greatest weakness" the weak answer offers a disguised strength ("I work too hard") -- interviewers are trained to recognize this and it reduces credibility on self-awareness.

**What it signals about the company:** When appropriate, explain what the phrasing of the question reveals about the organization's culture, values, or current pain points. "We move fast and break things" in a JD predicts a question about high-velocity decision-making -- which also reveals the company probably has some technical or process debt it is managing.

---

### Step 6: Perform a Gap Analysis Between the User's Background and the JD

This is the highest-value section of the output. Interviewers probe mismatches between the JD and the resume with some of the hardest questions, and most candidates are underprepared for these.

**How to identify gaps:**
- Compare the user's stated background against each required skill in the JD
- Flag any required experience the user has never done (no exposure)
- Flag any required skill the user has done but at a lower level of scope, scale, or seniority than the role requires
- Flag industry or domain mismatches (e.g., user comes from B2C, role is B2B; user comes from a startup, company is enterprise)
- Flag credential gaps (the JD says "MBA preferred" and the user does not have one)

**For each gap, generate:**
- The specific question the gap will trigger
- A bridging strategy -- how the user can address the gap honestly without appearing underqualified. The best bridging strategies acknowledge the gap directly, reframe relevant analogous experience, and demonstrate learning orientation and a specific plan

**Do not generate false gaps.** If the user's background clearly covers a requirement, do not flag it as a gap to appear thorough. Gap inflation wastes the user's preparation time and creates unnecessary anxiety.

---

### Step 7: Generate Questions the User Should Ask the Interviewer

Produce 4-6 questions the user should ask at the end of the interview, mapped to the interviewer type. These serve three purposes:
1. They demonstrate genuine preparation and strategic thinking
2. They reveal real information about the role and company that helps the user decide whether to accept an offer
3. They extend a positive impression into the last minutes of the interview

Each question should include:
- The question text itself (specific, not generic)
- Why the question impresses this specific interviewer (HR, hiring manager, peer, executive)
- What the answer reveals about the role, team, or company that is worth knowing

Calibrate the questions to the interviewer type:
- Questions for HR: compensation structure, benefits, team composition, timeline
- Questions for hiring managers: how success is measured in the first 90/180/365 days, what the team's biggest current challenge is, why the last person left
- Questions for peers: what a typical day looks like, what they wish they had known before joining, how decisions get made on the team
- Questions for executives: company vision for this function over the next 2-3 years, how they think about the problem space, what leadership style works best in this organization

---

### Step 8: Assign Likelihood Tiers and Finalize the Ranked Output

Assign every question to one of three likelihood tiers:

**Almost Certain (3-5 questions):** These appear in nearly 100% of interviews for this role type at this stage, regardless of the specific company or JD. They include: "Walk me through your background / tell me about yourself," "Why are you interested in this role and company?", the top 2-3 requirements from the first section of the JD, and any obvious resume-to-JD mismatches.

**Highly Likely (4-6 questions):** These are triggered by specific language in the JD or the stated interview format. They would not appear at every interview for this role type, but they are predictable from this specific posting. Interviewers will ask these if there is time after the almost-certain questions.

**Possible (3-4 questions):** These depend on the interviewer's individual style, the pace of the conversation, or topics that come up naturally from prior answers. They represent the "tail" of the probability distribution. The user should be aware of them but should not prioritize preparation for these over the almost-certain and highly-likely tiers.

Total across all tiers: 10-15 questions. Provide the final output in the structured format below.

---

## Output Format

```
## Interview Question Forecast: [Role Title] at [Company Name]

**Interview stage:** [Recruiter screen / Hiring manager round / Technical round / Panel / Final round / Executive]
**Interviewer:** [Name/title if known, otherwise function: HR Recruiter / Hiring Manager (VP of X) / Peer team member / Cross-functional stakeholder / Executive]
**Interview format signals:** [Behavioral / Technical / Mixed / Case-style / Portfolio review / Unstructured / Unknown]
**Total questions predicted:** [10-15]
**Key JD signals identified:** [3-5 highest-priority phrases from the JD that drive the question set]

---

### Tier 1: Almost Certain (Prepare These First)

| # | Question | Category | What It Really Tests | JD / Context Trigger |
|---|----------|----------|---------------------|----------------------|
| 1 | "[Exact question phrasing]" | [Behavioral / Technical / Situational / Role-specific] | [The actual competency, not the topic] | "[Exact JD phrase or context signal]" |
| 2 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[JD phrase]" |
| 3 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[JD phrase]" |
| 4 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[JD phrase]" |

### Tier 2: Highly Likely (Prepare These Second)

| # | Question | Category | What It Really Tests | JD / Context Trigger |
|---|----------|----------|---------------------|----------------------|
| 5 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[JD phrase]" |
| 6 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[JD phrase]" |
| 7 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[JD phrase]" |
| 8 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[JD phrase]" |
| 9 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[JD phrase]" |

### Tier 3: Possible (Prepare If Time Allows)

| # | Question | Category | What It Really Tests | JD / Context Trigger |
|---|----------|----------|---------------------|----------------------|
| 10 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[JD phrase / interviewer style]" |
| 11 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[Trigger]" |
| 12 | "[Exact question phrasing]" | [Category] | [Actual competency] | "[Trigger]" |

---

### Deep Preparation Notes

*(Provided for Tier 1 and high-priority Tier 2 questions. For remaining questions, preparation notes are abbreviated.)*

---

**Q1: "[Question]"**
- **What it tests:** [2-4 sentence explanation of the underlying competency -- go beyond the obvious]
- **Strong answer includes:** [3-4 specific elements that differentiate a good answer from a great one]
- **Weak answer looks like:** [2-3 specific failure modes, named concretely]
- **What this question reveals about the company:** [1-2 sentences about what the phrasing signals about the organization]
- **Prep note for you:** [Personalized based on the user's stated background -- which of their experiences is the right source material for this answer?]

**Q2: "[Question]"**
- **What it tests:** [...]
- **Strong answer includes:** [...]
- **Weak answer looks like:** [...]
- **What this question reveals about the company:** [...]
- **Prep note for you:** [...]

*(Continue for each Tier 1 question and high-priority Tier 2 questions)*

---

### Gap Analysis: Where Your Background May Be Questioned

| Gap Type | Specific Gap | Likely Question | Bridging Strategy |
|----------|-------------|----------------|-------------------|
| [No exposure / Limited scope / Domain mismatch / Credential gap] | [Exact gap description] | "[The question this generates]" | [Specific strategy: acknowledge + reframe + learning plan] |

---

### Questions to Ask the Interviewer

| # | Question to Ask | Interviewer Type | Why It Impresses | What the Answer Reveals |
|---|----------------|-----------------|-----------------|------------------------|
| 1 | "[Specific, non-generic question]" | [HR / Hiring manager / Peer / Executive] | [Why this question lands well with this specific interviewer] | [What you learn from their answer] |
| 2 | "[Specific, non-generic question]" | [Type] | [...] | [...] |
| 3 | "[Specific, non-generic question]" | [Type] | [...] | [...] |
| 4 | "[Specific, non-generic question]" | [Type] | [...] | [...] |

---

### Preparation Priority Order

1. [Question number and abbreviated title] -- [One sentence on why this is highest priority]
2. [Question number and abbreviated title] -- [One sentence]
3. [Continue for all Tier 1 and Tier 2 questions in priority order]
```

---

## Rules

1. **Every question must be traceable to the job description or to established interview norms for the role type and stage.** If a question cannot be linked to a specific JD phrase or a near-universal interview pattern, it belongs in Tier 3 (Possible) or should be excluded entirely. Never invent questions from thin air and label them "highly likely."

2. **Question text must be specific and usable as a rehearsal prompt.** Write questions in the exact phrasing an interviewer would use. "Tell me about a time you prioritized competing demands from engineering and sales while managing a quarterly roadmap" is a usable rehearsal prompt. "Tell me about prioritization" is not.

3. **The hidden subtext section is not optional.** Every predicted question must include what the question actually tests beneath the surface. The surface topic (conflict with a manager) and the actual competency being evaluated (psychological safety, upward influence, professional judgment) are different things. Failing to distinguish these leads users to prepare the wrong answer.

4. **Calibrate question depth to the exact seniority level stated in the JD.** A senior individual contributor asked "Tell me about a time you drove alignment across teams" gets a different expected answer than a director. Explicitly flag whether the expected answer involves personal execution, team direction, or organizational strategy. A mismatch between stated seniority and answer depth is one of the most common interview failure modes.

5. **The gap analysis section must be honest and specific, not encouraging or hedged.** If the user has zero A/B testing experience and the JD lists it as required, say so directly and give a realistic bridging strategy. Do not soften the gap with phrases like "you probably have some transferable experience." Underestimating a gap leaves the user underprepared for the hardest questions they will face.

6. **Never include questions that are legally prohibited in employment interviews in most jurisdictions.** These include questions about age, national origin, religion, disability status, pregnancy or plans for pregnancy, marital or family status, sexual orientation, or financial situation beyond job-relevant credit history. If the user asks whether to prepare for such questions, acknowledge the legal context and redirect to legitimate interview prep.

7. **Interview stage filtering is mandatory.** A recruiter screen produces a different question set than a final-round executive conversation. Never output a question set calibrated for a hiring manager round when the user stated they are having a recruiter screen, and vice versa. The stage filter changes the distribution significantly: recruiter screens are 80% role-specific and fit questions; technical rounds are 70-80% technical and situational; executive rounds are 60-70% strategic and values-based.

8. **The "Questions to Ask the Interviewer" section must be specific to the stated interviewer type.** Generic questions like "What does success look like in this role?" are acceptable as baseline but should be supplemented with questions that demonstrate knowledge of this specific company, industry, or role challenge. Generic questions to ask reveal a lack of research; specific questions reveal preparation depth.

9. **Total question count must stay between 10 and 15.** Below 10 signals underprepared analysis. Above 15 creates cognitive overload that prevents the user from prioritizing meaningfully. If the JD has 20+ requirements, the question set should still be capped at 15 and should reflect the top-priority requirements, not an exhaustive list.

10. **Do not generate preparation notes that direct the user to write full STAR answers.** This skill identifies and decodes questions; it does not produce answer scripts. Instead, preparation notes should identify which specific experiences from the user's background are the best source material for each question, and flag the 1-2 most important elements to include in the answer. For full answer construction, redirect to `behavioral-interview-prep`.

11. **When the company stage affects likely question style, call it out explicitly.** A Series B startup and a Fortune 500 company interviewing for the same PM title will ask materially different questions. The startup wants to know if you can own a problem end-to-end with limited resources and high ambiguity. The Fortune 500 wants to know if you can operate within process, influence without authority at scale, and manage stakeholder complexity. Label this in the output header.

12. **Never present a question as "almost certain" based solely on it being a common interview question.** Almost-certain status requires either a direct JD trigger, a clear resume-to-JD mismatch, or a near-universal pattern for the specific stage and interviewer type. "What is your greatest weakness?" is common but is not "almost certain" for a technical skills round with a peer interviewer. Stage and interviewer context must validate the likelihood tier.

---

## Edge Cases

### No Job Description Available
When the user has no JD -- informal referral, internal move not yet posted, networking conversation -- proceed using role title, company stage, and industry as the primary signals. Explicitly label all questions as "estimated from role norms" rather than "JD-anchored." Generate a set of 8-10 questions using industry-standard interview patterns for the role type and level. State clearly at the top of the output: "This forecast is based on role and industry norms, not a specific job description. Prediction precision increases significantly with a JD. Before your interview, ask your recruiter or contact for a job description or role overview document, even informally." Acknowledge that gap analysis cannot be completed without a JD.

### Multi-Interviewer Panel in a Single Day
When the user will meet multiple interviewers in a single day (a panel or loop format common in tech companies), restructure the output by interviewer rather than by likelihood tier. Create a separate question section for each interviewer type: HR/recruiter, hiring manager, peer team member, cross-functional stakeholder, and executive. Within each section, note that interviewers coordinate minimally, so the user should expect to answer variations of the same behavioral questions multiple times. Advise the user to maintain consistent story selection across interviewers -- experienced panelists debrief and notice when a candidate tells materially different stories to different interviewers about the same situation.

### Internal Promotion or Lateral Move
An internal interview is structurally different from an external one. The company already has data about the user. The questions shift from "can you do this job?" to "why should it be you over an external candidate?" and "how will you handle the dynamics of leading former peers?" Generate specific questions unique to internal promotions: "How will you handle it if a colleague you used to work alongside as a peer now reports to you?", "What would you do differently in this role than the previous person did?", "What is your vision for how this team should operate?", and "Why do you want this role now -- what's changed for you?" Also flag the specific dynamic that internal candidates often under-prepare because they feel comfortable with the company, which is one of the most common failure modes for internal promotion interviews.

### Very Long Job Description (25+ Requirements)
Long job descriptions almost always contain a mix of genuine requirements and aspirational wish-list items that were added by committee. Parse them using this prioritization rule: requirements in the first 3-5 bullets of each section are highest priority; items buried near the bottom of a long list ("nice to have" or "bonus" sections) are lowest priority. Interviewers cannot probe 25 requirements in 45 minutes -- the actual interview will cover 7-10 topics at most. Focus the question forecast on the opening requirements, the requirements repeated in multiple forms, and any requirement that appears in both the responsibilities section and the qualifications section (repetition signals high priority). Label the de-prioritized requirements explicitly so the user understands why they are not in the question set.

### Career Pivot or Non-Traditional Background
When the user is changing industries, switching functions (e.g., engineering to product management), or entering a field they have adjacent but not direct experience in, the gap analysis section becomes the most important part of the output. Expand it significantly. For each gap, provide a specific bridging narrative structure: (1) acknowledge the different context directly rather than hoping the interviewer does not notice, (2) identify the closest analogous experience from the user's background and make the mapping explicit, (3) demonstrate domain awareness by showing knowledge of the target field's specific challenges, and (4) show a concrete learning plan (specific resources, timeline, or early actions in the role). Career pivoters face a version of every question that starts with the subtext "why should we take a risk on someone without direct experience?" -- every answer needs a bridge back to why the pivot makes sense and why this candidate is better positioned than a lateral hire might expect.

### The Interview Is in 24 Hours or Less
When the user has very little time before the interview, restructure the output to emphasize preparation efficiency. Lead with a "Prepare These Tonight (Priority Order)" section that lists only the 4-5 highest-likelihood questions with the most critical preparation notes. Explicitly tell the user which sections to skip for now. Do not output the full 15-question set in a way that buries the highest-leverage questions. The user should spend 60% of their remaining time on Tier 1 questions, 30% on the gap analysis, and 10% on questions to ask. A preparation note at the top of the output should read: "Given your timeline, prioritize Tier 1 questions and the gap analysis. Do not try to prepare for all 15 questions tonight -- this increases anxiety without improving performance."

### The User Has Been in the Same Role or Company for 7+ Years
Long-tenured candidates face a specific set of predictable questions that are not generated by the JD itself but by the resume context. These include: "Why are you leaving after X years?", "How have you kept your skills current during that time?", "How do you think your experience in one environment will translate to our context?", and -- depending on the new company's size -- "What makes you confident you can operate in a [larger/smaller/faster/more structured] environment?" These should appear in the gap analysis and as dedicated Tier 1 or Tier 2 questions, because interviewers reliably probe the transition story for long-tenured candidates. Flag that the user's answer to "why are you leaving?" will be scrutinized more carefully for a 7-year tenure than for a 2-year tenure.

### Interviewing After a Layoff, Gap, or Involuntary Departure
Candidates with employment gaps, recent layoffs from high-profile company failures, or any departure framing that could read as "fired" face predictable probing. Generate specific questions around the departure context: "Walk me through what happened at [Company] from your perspective," "What did you do during the gap between [end date] and now?", and "What did you learn from that experience?" For each, the bridging strategy follows the same structure: factual and non-defensive description of what happened, evidence of agency and reflection during the gap or transition, and forward-facing narrative about what the user is looking for now and why this role is the right next step. Caution the user that over-explaining, over-defending, or speaking negatively about former employers are the three most damaging failure modes for these questions.

---

## Example

**User Input:**
"I have a first-round interview for a Senior Data Analyst role at a Series C fintech company (~300 employees). The JD says: 'Partner with finance, product, and marketing to deliver data-driven insights that influence key business decisions. Own our self-serve analytics infrastructure using dbt and Looker. 4+ years of SQL experience required, Python preferred. Experience in financial services or fintech preferred. Design and maintain dashboards used by C-suite executives. Drive adoption of a data-as-a-product culture.' The interview is with the Head of Data (hiring manager), 60 minutes. I have 5 years of analytics experience at a retail e-commerce company, strong SQL and Tableau (not Looker), Python at a beginner level, no financial services background."

---

**Full Output:**

## Interview Question Forecast: Senior Data Analyst at [Fintech Company]

**Interview stage:** Hiring manager first round (60 minutes)
**Interviewer:** Head of Data
**Interview format signals:** Mixed -- expect behavioral and technical probing with situational scenarios about the data infrastructure ownership
**Total questions predicted:** 13
**Key JD signals identified:** "Own our self-serve analytics infrastructure" (ownership language), "influence key business decisions" (executive-stakeholder visibility), "dbt and Looker" (specific tool stack), "data-as-a-product culture" (organizational change management), "C-suite dashboards" (executive communication)
**Company stage note:** Series C fintech (~300 employees) means the company is scaling but not fully mature. Expect questions about building systems under ambiguity, cross-functional influence without process support, and comfort with evolving requirements. This is not a caretaker role -- "own" and "drive adoption" are high-agency signals.

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### Tier 1: Almost Certain (Prepare These First)

| # | Question | Category | What It Really Tests | JD / Context Trigger |
|---|----------|----------|---------------------|----------------------|
| 1 | "Walk me through your analytics background and what draws you to this role specifically." | Role-specific | Coherent career narrative, genuine motivation, self-awareness about fit | Standard hiring manager opener; 5-year tenure at one company |
| 2 | "Tell me about a project where you used SQL to answer a complex business question. Walk me through the query logic and the business impact." | Technical | SQL depth, problem decomposition, ability to connect technical work to business outcomes | "4+ years of SQL experience required" |
| 3 | "Tell me about a time you built or owned an analytics product that non-technical stakeholders depended on. How did you ensure adoption and quality?" | Behavioral | Ownership mindset, stakeholder management, data product thinking | "Own our self-serve analytics infrastructure" + "data-as-a-product culture" |
| 4 | "Describe a dashboard or report you built for a senior leader. How did you decide what to include, and how did you handle their feedback?" | Behavioral | Executive communication, information hierarchy judgment, professional influence | "Design and maintain dashboards used by C-suite executives" |

### Tier 2: Highly Likely (Prepare These Second)

| # | Question | Category | What It Really Tests | JD / Context Trigger |
|---|----------|----------|---------------------|----------------------|
| 5 | "What is your experience with dbt and Looker? Walk me through how you have used them in practice." | Technical | Specific tool proficiency; if no Looker experience, gap acknowledgment and learning orientation | "Own our self-serve analytics infrastructure using dbt and Looker" |
| 6 | "Tell me about a time you had to influence a business decision using data when the stakeholder initially disagreed with your finding." | Behavioral | Analytical courage, influence without authority, data storytelling | "Deliver data-driven insights that influence key business decisions" |
| 7 | "How have you worked cross-functionally with finance, product, or marketing teams? What did you learn about how different functions interpret and use data?" | Behavioral | Cross-functional empathy, ability to translate between technical and business contexts | "Partner with finance, product, and marketing" |
| 8 | "What is your experience with Python? How do you use it in your analytics workflow?" | Technical | Python depth and honest self-assessment; gap acknowledgment | "Python preferred" |
| 9 | "How would you approach getting a team of business stakeholders to shift from ad-hoc data requests to self-serve analytics?" | Situational | Change management, user empathy, systems thinking, organizational influence | "Drive adoption of a data-as-a-product culture" |

### Tier 3: Possible (Prepare If Time Allows)

| # | Question | Category | What It Really Tests | JD / Context Trigger |
|---|----------|----------|---------------------|----------------------|
| 10 | "What do you know about the fintech industry and the specific data challenges that are different from other sectors?" | Technical / Domain | Domain learning orientation, research depth, intellectual curiosity | "Financial services or fintech preferred" + gap in user's background |
| 11 | "Tell me about a time you found an error or quality issue in a dataset or report that was already in production. What did you do?" | Behavioral | Data integrity standards, accountability, proactive communication | Implied by "Own" infrastructure language at a Series C with high executive visibility |
| 12 | "Where do you see the role of data in a company at this stage of growth, and how do you want to grow professionally?" | Role-specific | Long-term retention signal, vision alignment, ambition calibration | Head of Data evaluating career fit for Series C scaling environment |
| 13 | "What is your approach to documentation and data governance for analytics infrastructure?" | Technical | Systems thinking, professionalism, quality habits | "Own" infrastructure at a company without mature data practices |

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### Deep Preparation Notes

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**Q1: "Walk me through your analytics background and what draws you to this role specifically."**
- **What it tests:** The Head of Data is evaluating whether the user's career story is coherent (each role built on the previous one), whether they have a clear reason for this specific move (not just "I want a new job"), and whether their stated motivation matches the actual challenges of the role. A candidate who says "I want to drive data culture" but whose background is purely technical report delivery will not be credible.
- **Strong answer includes:** A 90-second career narrative that highlights progression (not just chronology), specific mention of why the shift from e-commerce analytics to fintech is a deliberate move rather than an opportunistic one, and a direct connection between something specific in the JD (the dbt/Looker ownership, the C-suite visibility) and something the user has demonstrated before.
- **Weak answer looks like:** A chronological job history recitation without synthesis ("I started at X, then went to Y, then Z"). Mentioning the company name but not the specific role -- "I've always loved fintech" without being able to say what about this data team's challenge is compelling.
- **What this question reveals about the company:** The Head of Data wants someone who sees data as a strategic function, not a service org. The phrase "data-as-a-product culture" in the JD signals that they are trying to change how the company thinks about data -- they want a candidate who has conviction about that vision, not just technical skills.
- **Prep note for you:** Your 5-year tenure at a retail e-commerce company is unusual -- most candidates at this level have 2-3 companies. This will read either as impressive loyalty and depth, or as limited exposure to different environments. Frame it as depth: you built the analytics capability from early stage and saw it through scale. Then make the fintech pivot feel intentional: what is it about financial data specifically (transaction-level granularity, regulatory complexity, real-time risk signals) that makes this a deliberate next step?

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**Q2: "Tell me about a project where you used SQL to answer a complex business question. Walk me through the query logic and the business impact."**
- **What it tests:** SQL listed as "required" at 4+ years means the interviewer will not take self-reported proficiency at face value. The Head of Data is evaluating: Can this person write queries that handle real complexity (multi-table joins, window functions, CTEs for readability, performance optimization)? And can they connect technical work to a business outcome without losing the non-technical thread? Technical depth AND communication clarity must both be present.
- **Strong answer includes:** A specific project with a named business question ("We needed to understand why customer LTV dropped 18% in Q3"). The SQL approach described at a conceptual level -- what tables, what logic, why that approach over alternatives. A concrete outcome with a number ("the analysis changed our retention strategy for that cohort and recovered approximately $2.1M in annualized revenue").
- **Weak answer looks like:** A vague answer about "pulling a lot of data from multiple tables." An answer that is all technical and never explains the business context. An answer that is all business context and skips the SQL mechanics entirely -- this signals the candidate wants to avoid demonstrating technical depth.
- **What this question reveals about the company:** The Head of Data at a Series C fintech without a dedicated data engineering team likely needs analysts who can own complex queries end-to-end without hand-holding. Self-sufficiency in SQL is a survival requirement, not a differentiator.
- **Prep note for you:** Choose a project from your e-commerce background where the SQL was genuinely non-trivial -- ideally one involving customer behavior modeling, cohort analysis, or funnel analysis (these translate directly to fintech LTV/churn contexts). If you have used window functions, CTEs, or query optimization in your work, make that explicit -- these are the markers of senior SQL proficiency that the interviewer will be listening for.

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**Q3: "Tell me about a time you built or owned an analytics product that non-technical stakeholders depended on. How did you ensure adoption and quality?"**
- **What it tests:** "Own" is the key word in the JD, and this question evaluates whether the user genuinely owned an output (designed it, maintained it, iterated on it based on feedback, ensured data quality) or whether they simply built something once and handed it off. The Head of Data is also testing for user empathy -- did the candidate think about how non-technical people actually use the product, or did they build for technical elegance?
- **Strong answer includes:** Evidence of end-to-end ownership (not just building but also maintaining, troubleshooting, and iterating). A specific strategy for ensuring stakeholder adoption -- user interviews before building, training sessions, feedback loops, usage monitoring. A concrete quality mechanism -- how did you catch and handle data errors before they reached stakeholders? An outcome: what changed in how the business operated because of this product?
- **Weak answer looks like:** "I built a dashboard and sent it to the team." No mention of adoption challenges or how they were overcome. No mention of data quality processes -- this signals the candidate does not think about the production reliability requirements of critical reporting.
- **What this question reveals about the company:** The "self-serve analytics infrastructure" language combined with "drive adoption of a data-as-a-product culture" strongly suggests that the current state is a fragmented mix of ad-hoc requests and inconsistent reporting. The company wants someone who has been through the process of building structured analytics access for non-technical users and knows how hard adoption actually is.
- **Prep note for you:** Your Tableau experience is strong source material here even though the company uses Looker. The tool is different; the ownership and adoption challenge is structurally identical. Pick your strongest example of a dashboard or reporting product that business stakeholders genuinely relied on -- the closer it is to a product that had scheduled refreshes, clear ownership, and user feedback incorporated over time, the better.

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**Q4: "Describe a dashboard or report you built for a senior leader. How did you decide what to include, and how did you handle their feedback?"**
- **What it tests:** C-suite visibility is explicitly listed as a key responsibility. The Head of Data is evaluating: Does this candidate understand that executive dashboards are fundamentally different from operational dashboards? Executives need decision-oriented information -- not comprehensive data. The question also tests professional presence: can the candidate handle unclear, conflicting, or demanding feedback from a senior leader without becoming defensive or paralyzed?
- **Strong answer includes:** A specific example at the VP, SVP, or C-suite level. Explicit description of the information hierarchy decision -- why these 3-4 metrics and not the other 12. A concrete description of how stakeholder feedback changed the design, including at least one instance where the leader's request was actually wrong and how the candidate navigated that. Outcome: did the executive use it? Did it change anything?
- **Weak answer looks like:** An answer about a "leadership dashboard" that was actually used by mid-level managers. An answer where the candidate simply did whatever the executive asked with no pushback or independent judgment. An answer that focuses on the technical construction of the dashboard rather than the information design decisions.
- **What this question reveals about the company:** C-suite dashboard ownership at a 300-person Series C means this role has genuine executive visibility -- this is high-stakes work that will be scrutinized. The Head of Data wants to know the candidate will not embarrass the data team by sending a confusing or inaccurate report to the CEO.
- **Prep note for you:** Think through your e-commerce experience for the highest-seniority stakeholder you built reporting for. Even if it was a VP rather than a C-suite exec, the principles are the same. The key element to prepare: a specific moment where you had to tell a senior leader "that metric will mislead because X -- here is what I recommend instead." If you have that story, it is gold. If you do not, think about an adjacent moment of analytical judgment under senior scrutiny.

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**Q5: "What is your experience with dbt and Looker? Walk me through how you have used them in practice."**
- **What it tests:** These are the specific tools named in the JD for a core responsibility. The Head of Data is testing whether the candidate has genuine hands-on experience or is pattern-matching on buzzwords. For dbt specifically, a strong answer demonstrates understanding of the transformation layer -- models, tests, documentation, the ref() function, incremental models. For Looker, a strong answer demonstrates understanding of LookML, the semantic layer concept, and the self-serve philosophy behind Looker's architecture.
- **Strong answer includes:** For dbt: specific experience with dbt models, the testing framework (schema tests, custom tests), and the importance of documentation as a first-class concern. For Looker: LookML dimension/measure definitions, the separation of SQL logic from presentation, and how the semantic layer enables non-technical self-serve.
- **Weak answer looks like:** "I have heard of dbt and am a fast learner." Or a description of Tableau workflows presented as if they are equivalent to Looker. Tool substitution without acknowledgment of the real differences is a red flag.
- **Gap bridging strategy for you:** You have Tableau, not Looker. Acknowledge this directly: "I have not worked in Looker specifically, but I have 5 years of deep Tableau experience. I understand the architectural shift Looker represents -- the semantic layer and LookML approach is a different philosophy from Tableau's data connection model, and I have studied how it works. I am confident in my ability to ramp on the tool; I have done it before when I transitioned from [prior tool] to Tableau." For dbt, check whether you have any exposure to SQL-based transformation layers -- even experience with stored procedures or views in a data warehouse context demonstrates the underlying concept.

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### Gap Analysis: Where Your Background May Be Questioned

| Gap Type | Specific Gap | Likely Question | Bridging Strategy |
|----------|-------------|----------------|-------------------|
| Tool mismatch | Tableau experience, not Looker; no stated dbt experience | "You have Tableau listed but we use Looker. How quickly can you get up to speed, and how would you approach it?" | Acknowledge directly. Emphasize the conceptual understanding of BI tool architecture and the record of having learned tools before. Offer a concrete ramp timeline: "I would expect to be building in Looker independently within 3-4 weeks -- here is how I would approach the learning." Avoid saying "they're basically the same" -- they are not. |
| Skill depth gap | Python listed as "preferred"; user is at beginner level | "Our team uses Python for more complex analyses. Walk me through your Python experience." | Be honest about the level (beginner/intermediate). Name the specific things you can do in Python currently. Name the specific things you cannot yet do. Describe your learning plan with a concrete timeline and resource. Do not inflate Python proficiency -- the Head of Data will almost certainly ask a follow-up that exposes the gap. |
| Domain mismatch | No financial services or fintech experience; all experience in e-commerce | "Our data has a lot of domain-specific complexity -- transaction risk, regulatory reporting, LTV modeling with subscription and lending products. How would you get up to speed on the fintech-specific context?" | Acknowledge the domain gap. Demonstrate that you have already done some domain research (name one or two specific things about fintech data that are different from e-commerce: chargeback data, cohort-based LTV for lending, real-time fraud signals, regulatory compliance reporting). Show intellectual curiosity about the domain. Retail e-commerce has genuinely transferable skills: customer LTV, funnel analysis, and conversion optimization map directly to fintech customer acquisition and retention metrics. |

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### Questions to Ask the Head of Data

| # | Question to Ask | Why It Impresses | What the Answer Reveals |
|---|----------------|-----------------|------------------------|
| 1 | "The JD mentions driving adoption of a data-as-a-product culture. Where is the company today on that journey -- what does the current state look like, and what has gotten in the way?" | Shows you understand that "data-as-a-product" is a change management challenge, not a technical one -- and that you have thought about what building it actually requires | Reveals whether this role is building something from scratch, inheriting a partial implementation