[NAME] — Data Engineer / Analytics Engineer
[City, RO] (optional) · [Email] · [Phone] (optional) · [LinkedIn] · [GitHub] · [Portfolio]

SUMMARY
2–4 lines: what pipelines/models you built, how you ensure data quality, and the business impact you delivered.

DATA HIGHLIGHTS
- Pipelines: sources → warehouse + outcome (latency, stability)
- Data quality: checks/monitoring + fewer inconsistencies
- Modeling/consumption: metrics, dashboards, consistent definitions

EXPERIENCE
[Company] — [Role] · [Period] · [City/Remote]
- Pipeline/orchestration + outcome
- Data quality + outcome
- Modeling/BI + outcome
- Cost/performance optimizations (if applicable)

[Company] — [Role] · [Period]
- 2–4 bullets

SELECTED PROJECTS (optional)
[Project] — [Link]
- Dataset + model + the decision it enabled

SKILLS
SQL: …
Orchestration: …
Warehouse/Lake: …
BI: …
Testing/Quality: …

EDUCATION & CERTIFICATIONS
[University / Course] — [Period]
[Certification] — [Year]