A data CV should show: what data you moved, how you ensured quality, and what decisions became possible (metrics, dashboards, cost/time improvements).
This guide is a role-specific companion to the general CV structure: IT CV template (Romania).
TL;DR
- Put these early: pipelines, data quality, modeling, and who consumes the outputs (product/finance/ops).
- Mention latency/refresh and scale (use ranges if you can’t share exact numbers).
- Don’t list tools without context: connect them to outcomes (accuracy, speed, reliability, cost).
Quick checklist
- Clear headline: “Data Engineer” / “Analytics Engineer” / “BI Developer”.
- 3–6 strong bullets: pipelines, quality checks, modeling, metric definitions.
- Mention ownership: definitions, documentation, and reliability practices.
Recommended structure (Data)
- Header (clean links)
- Summary (2–4 lines: domain + strengths)
- Experience (data flows + impact)
- Selected projects (optional)
- Skills (SQL, orchestration, warehouse, BI, testing)
- Education/certifications (short)
What a good data bullet looks like
A strong bullet includes: (1) the flow, (2) what you changed, (3) quality guarantees, (4) the outcome for consumers.
Examples:
- “Built an incremental pipeline for [source] with monitoring and quality checks, reducing reporting latency from [X] to [Y].”
- “Standardized the definition of a key metric (e.g., churn) and documented it, reducing inconsistent interpretations across teams.”
If you can’t share scale, use ranges and signals:
- “millions of events/day”, “dozens of sources”, “refresh every 15 min”, “50+ dashboards”.
Bullet library (Data)
Pick 6–10 and adapt them to your real work.
Pipelines & orchestration
- “Built pipelines for [sources] with orchestration and retries, improving reliability and reducing failures.”
- “Introduced incremental loads and controlled backfills, reducing processing time and risk.”
- “Stabilized an unreliable batch job via retry/backoff and monitoring, reducing missed refreshes.”
- “Added pipeline observability (success/failure, latency, freshness) to reduce surprises in production.”
Data quality & governance
- “Introduced data quality checks (schema/nulls/ranges), reducing reporting inconsistencies.”
- “Created a data contract for an upstream source, reducing breaking changes.”
- “Standardized metric definitions and ownership, reducing confusion across teams.”
- “Added documentation/lineage for critical datasets, speeding up onboarding and debugging.”
Modeling & consumption
- “Built a clean analytical model for [domain] that improved consistency and maintainability across dashboards.”
- “Reduced dashboard query time by optimizing the model and the underlying SQL.”
- “Introduced anomaly detection/alerts on key metrics, improving reaction time to issues.”
- “Partnered with stakeholders to define KPIs and ensure consistent interpretation.”
Cost & performance
- “Optimized warehouse costs via partitioning/clustering and query tuning, reducing spend.”
- “Reduced refresh time for a critical dashboard via caching and incremental aggregates.”
Sub-role examples (use what matches your work)
Data Engineer
- “Improved pipeline reliability by adding monitoring and standardized retry behavior.”
- “Reduced processing time and cost by introducing partitioning and incremental loads.”
Analytics Engineer
- “Implemented model tests and documentation, reducing errors and duplicated logic.”
- “Standardized metric definitions to remove ambiguity between teams.”
BI / Reporting
- “Refactored legacy reports for consistency and performance, improving stakeholder trust.”
- “Built dashboards with clear definitions and data freshness guarantees.”
Common mistakes
- “Used X/Y/Z” without explaining what you shipped with it.
- Dashboards described without who uses them and what decisions they support.
- Unclear split between data engineer vs analytics engineer vs BI.
- No mention of freshness/latency/quality — core signals in data roles.
Useful keywords (use only what you actually did)
- SQL, data modeling, warehouse/lakehouse
- orchestration (Airflow/…)
- ETL/ELT, incremental loads, backfills
- data quality/testing, observability
- BI (Looker/PowerBI/Tableau) if applicable
Data CV template (copy/paste)
FAQ
Should I list tools (Airflow/dbt/warehouse)?
Yes, but only if you used them in real deliveries. The tool matters less than the outcome (freshness, quality, cost, maintainability).
How do I show impact without numbers?
Use operational outcomes: fewer failures, faster refresh, fewer manual reconciliations, consistent definitions, faster debugging.
