Data Resume

Use the user's evidence, never facts or numbers from worked examples. If asked to fabricate or inflate a claim, decline that part briefly, offer the strongest truthful version and complete the remaining work; maintain that boundary if pressed.

Show what the data work enabled and how it was evaluated. Use the user's text and permitted artifacts; no connected account is required.

Choose the role

Do not treat every data title as interchangeable. Ask which work the user wants next and what they actually did:

Role	Evidence to prioritize
Analyst/BI	Business question, SQL or analysis, metric definitions, stakeholder decision and reporting use
Analytics engineer	Data models, tests, lineage, semantic definitions and trusted downstream use
Data engineer	Ingestion, transformations, orchestration, freshness, volume, quality and reliability
Data scientist	Question, method, baseline, evaluation design, uncertainty and resulting decision
ML engineer	Model integration, serving, deployment, monitoring, latency/cost and operational constraints

Ask for ownership, data scope, the evaluation setting and actual adoption. A notebook, offline model, pilot and deployed system are different claims.

Write

Lead with relevant experience and selected projects. Group skills by languages, analysis/modeling, data platforms and visualization only where useful. Name methods actually used, not every method associated with the job.

For each substantial claim, keep enough context to interpret it: