Data and AI Interview Prep

The 50 Questions That Actually Show Up in DE and AI Interviews

Real questions from real interviews. Model answers that explain what the hiring manager actually wants to hear. Because knowing the answer is not enough if you cannot frame it right.

FREE 10-question cheat sheet via email · Full pack with scoring rubrics and 7-day plan

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150+ drills across analytics, ML, experimentation, and product sense 7-day prep plan with daily focus blocks

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Get the 10 Hardest DE Interview Questions (with model answers)

The specific questions candidates struggle with most, answered the way senior engineers answer them. Free.

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What is inside the drill pack

Structured, repeatable interview reps built for candidates who want focused preparation, not content overload.

Role-specific question banks

Curated drills for Data Analyst, Data Scientist, ML, and Analytics roles so you practice the patterns hiring teams actually use.

Answer frameworks and model responses

Clear response structures plus strong, average, and weak examples so you learn how to answer with precision.

Objective scoring rubrics

Interview-style rubrics for depth, clarity, and tradeoff thinking so you can self-review and improve fast.

Module breakdown

Focused modules that mirror how modern data and AI interviews are run.

Modern Data Stack

dbt, Apache Iceberg, and DuckDB

15 questions

  • How do you choose between dbt incremental strategies (append vs merge vs delete+insert)?
  • What is a custom materialization in dbt and when would you build one?
  • How do you write effective dbt tests beyond the built-in ones?
  • Explain dbt sources vs staging models. Why separate them?
  • What are dbt snapshots and when do you use them over regular models?

AI-Augmented Pipelines

LLMs, vector databases, feature stores

10 questions

  • How would you use an LLM to automate data quality checks in a pipeline?
  • What is structured output from an LLM and why does it matter for data engineering pipelines?
  • When would you add a vector database to a data platform? What problem does it solve?
  • What is a feature store and why does it matter for ML pipelines?
  • How do you manage the cost of LLM API calls in a production data pipeline?

Behavioral Scenarios

Stakeholder comms, incidents, data quality

6 scenarios

  • A data pipeline fails 2 hours before an executive dashboard meeting. Walk me through your response.
  • A business stakeholder insists a data number is wrong, but your pipeline shows it is correct. How do you handle it?
  • You discover 6 months of historical data was silently corrupted due to a schema change. What do you do?
  • You are asked to deliver a new data product in 2 weeks that realistically takes 6 weeks. What do you do?
  • Two downstream teams have conflicting definitions of the same business metric. How do you resolve it?

Why this works for DE and AI interviews

Most prep materials are generic. This pack tightens your practice to the exact skills that show up in data engineering and AI interviews.

Data Engineering track

  • System design drills for pipelines, reliability, and data quality
  • SQL and warehouse scenarios with performance tradeoffs
  • Modern data stack: dbt, Apache Iceberg, DuckDB
  • Behavioral scenarios for senior-level interviews
  • Behavioral and product sense questions tailored to DE roles

AI and ML track

  • Modeling questions with evaluation and error analysis
  • AI pipeline engineering questions
  • Experimentation and iteration drills for real production teams
  • Communication prompts for stakeholder clarity and impact

SAMPLE DRILL

Here is exactly what you get

One of 40 drills. SQL, system design, Python, and behavioral questions.

SQL Mid-level 20 min

Rolling 7-Day Revenue Average

The Question

We have a table called daily_revenue with columns: date (DATE), revenue (NUMERIC). One row per day, some days may be missing.

Write a query that returns each date with the 7-day rolling average revenue (current day + 6 prior days). Then explain: what changes if the average uses calendar days vs. recorded days only?

Model Answer

SELECT
    date,
    revenue,
    AVG(revenue) OVER (
        ORDER BY date
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) AS rolling_7d_avg
FROM daily_revenue
ORDER BY date;

Key insight: ROWS BETWEEN (not RANGE BETWEEN) is unambiguous with gap data. The window frame handles fewer-than-7-days automatically. The follow-up tests whether you understand calendar-day vs. recorded-day semantics and when each matters.

Scoring Rubric

Pass if:

  • Uses ROWS BETWEEN correctly and can explain why (not just RANGE)
  • Knows the window frame handles edge cases natively
  • Distinguishes calendar-day vs. recorded-day averages with a real explanation

Red flags:

  • Confuses ROWS and RANGE framing
  • Solves with a self-join instead of a window function
  • Adds CASE WHEN logic to handle edge cases the window handles automatically
SAMPLE

The full pack has 40 drills like this. See pricing below.

RK

Built by a practitioner, not a content mill

Ryan Kirsch

Data Engineer at the Philadelphia Inquirer

I've conducted 30+ technical screens as a hiring lead and been through 12+ data engineering interviews across media and tech companies. The patterns repeat. The gaps are predictable. I built this drill pack because I couldn't find one that actually matched how these interviews work in practice.

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Starter

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  • Core drill pack with 150+ questions
  • Answer templates and model responses
  • 7-day prep plan and mock interview script
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  • Everything in Starter
  • Advanced scoring rubrics and self-review checklists
  • Bonus: common failure patterns and fixes
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FAQ

Answers to the most common questions before you reserve presale access.

Who is this for?

Candidates preparing for data and AI interviews, especially Data Analyst, Data Scientist, ML, and Analytics roles. It is most useful if you are interviewing in the next 2 to 8 weeks.

Is this for beginners or experienced candidates?

Both. Beginners get structure and clear answer formats. Experienced candidates sharpen depth, speed, and communication under pressure.

How long does it take to complete?

The core plan is designed for 7 days. You can also use it as a reusable practice system for ongoing interview cycles.

Is this live coaching?

No. This is a self-serve drill pack with guided frameworks, model responses, and rubrics so you can practice independently.

When do I get access?

You reserve during presale now and receive access as soon as the MVP batch is released within the 7-day window.

What if it is not a fit for me?

If the pack does not match the scope described on this page, reply within 7 days of access and request a refund.

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