DataPrepAI. Interview prep built only for data roles.
DataPrepAI is an independent product built around one belief: data candidates deserve preparation that understands their actual interview loop, measures specific gaps, and tells the truth about what AI can and cannot assess.
The problem
Interview prep is built for software engineers and stretched over everyone else. A data loop is different: SQL under time pressure, a case study with no clean answer, metric definitions, experiment design, ML theory for some roles and pipeline design for others, and the mix changes with the title on the job post.
So candidates prepare from a list of questions and hope it was the right list. That list alone cannot tell you, before the real interview, which skills this job will test, how ready you are on each, or what to do about the weakest one this week.
Our approach
DataPrepAI exists to answer those three questions before the interview, not after. Every part of the product follows one rule: measure first, then practice what the measurement says.
Upload a resume and the job description. The gap analysis scores the fit skill by skill and shows what that loop will lean on. Mock interviews, typed or spoken, are scored against a rubric and the feedback names what to fix. A readiness score per skill moves with your results. Your roadmap is generated from your role, timeline, company type, and latest gap analysis; progress is tracked without pretending the plan silently rewrites itself. Run a second analysis for a similar role and your ratings carry over instead of starting from zero.
What makes it different
One audience. Six data roles, 108 skills in the catalog, and every question, rubric and roadmap written for them. There is no generic question bank underneath.
Scored against a consistent rubric. Each completed round is assessed for correctness, communication, structure, and depth, so the feedback is specific enough to act on.
Honest about the model. Results are generated by AI and checked against a fixed rubric, and the product says when a score is an estimate rather than a measurement.
Small by design. An independent product, changed weekly, with a real person reading every message.
In numbers
- 108 skills across 11 groups: Languages & querying (9), Databases & warehouses (10), BI & visualization (12), Data engineering & pipelines (13), Cloud & infrastructure (9), Machine learning (13), MLOps & deployment (8), Statistics & experimentation (9), Analytics & product (9), Data modeling & governance (9), Collaboration & communication (7)
- 6 roles: Data Analyst, Data Scientist, Data Engineer, ML Engineer, Analytics Engineer, BI Developer
- 6 interview round types: SQL, Python, ML theory, Case study, Behavioral, System design
- 91 public interview-question guides, all with worked walkthroughs
- 5 min Resume to plan
- 12 min Per day
- 1 Free gap analysis
- 2 Free scored mocks
- 7 days Money-back