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Interview · Technical

Data analyst

Covers investigating metric changes, SQL and data handling, basic statistics, experiments and presenting findings. Suits junior to senior analysts in product, business or reporting roles. Follow-ups push for the actual steps taken, how numbers were checked and what changed as a result.

Suits: Data analyst, Business analyst, Reporting analyst, Product analyst, Insights analyst

Questions
7 of 18
Length
35 min
Format
Voice or typed
Levels
Junior to Senior
See this interview on a 30-minute call.
AI interviewerScored
Example
  1. Interviewer: Tell me about a time you changed someone's mind at work. How did you go about it?

  2. Candidate: Our team wanted to rebuild the billing page from scratch, and I thought it was the wrong call, so we put together a plan to…

  3. Follow-up

    Interviewer: You said "we". What did you say yourself, and what finally changed their mind?

  4. Scored on

    • Analytical reasoning30%
    • SQL and data handling25%
    • Statistical judgement20%
    • Communication and impact25%

    Score, reasons and recording. Your team decides.

An example exchange. This interview's own questions open when you use it.

What it listens for, and how much each part counts.

Every answer is scored against this guide, with the reasoning written beside the score. You get the recording or the written answers too, and your team makes the call.

  • Analytical reasoning30%
    Breaks a vague question into steps and reaches a sound answer.
  • SQL and data handling25%
    Pulls, joins and checks data accurately.
  • Statistical judgement20%
    Knows when a result is real and what it can support.
  • Communication and impact25%
    Explains findings clearly so people act on them.
All interviews
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    Listens for

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    • Analytical approach
    • Technical accuracy
    • Recommendation and communication
    Voice or typed

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  • Data engineer

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    Finds out how a candidate builds reliable data pipelines and models that others can trust.

    Listens for

    • Pipeline design
    • Data modelling
    • Reliability and data quality
    • Trade-offs and collaboration
    Voice or typed

    7 of 17 questions · 35 min · Mid to Staff

  • Data scientist

    Data · Technical

    Finds out how a candidate frames problems, models with rigour and proves real-world impact.

    Listens for

    • Problem framing
    • Statistical and modelling rigour
    • Experimentation and causal reasoning
    • Communication and impact
    Voice or typed

    7 of 17 questions · 35 min · Mid to Staff