QA Engineer - Data;

QA Analyst - Microsoft Fabric / Data Lakehouse
Contract Independent Contributor

* Must be in Los Angeles area (hybrid)

* No 3rd parties
- No Sponsorship available
- No C2C

We're looking for a seasoned data QA specialist to serve as the sole, independent test function for a Microsoft Fabric medallion architecture build. You'll own the entire QA lifecycle - test case design through gate sign-off - validating data across Bronze, Silver, and Gold layers with no day-to-day direction.

• Author and maintain the full test-case library and unit-test catalog across all medallion layers, mapped to source-to-target mappings and grain statements
• Write repeatable SQL checks for column/type contracts, key uniqueness, grain, layer-to-layer reconciliation, referential integrity, and pipeline idempotence
• Produce test plans and evidence packs at each delivery gate (freeze, UAT, production promotion, hypercare) with written go/no-go recommendations and residual risk summaries
• Validate semantic model bindings, relationships, measures, and row-level security; confirm reports consume certified models rather than raw lake houses
• Log defects with layer attribution and severity - complete enough for engineers to act without a follow-up meeting
• Confirm CI and data-quality notebooks pass before deployment; verify promotion order, rollback readiness, and flag any exposed credentials as security defects
• Deliver a weekly written status covering coverage, pass rates, open defects, and risks to the next gate; elevate blockers same-day

• Hands-on data, ETL/ELT, or BI QA experience reconciling data across layers - not UI-only testing
• Demonstrated ability to author test cases and test plans from mappings, data dictionaries, and grain statements
• Strong SQL - joins, aggregation, window functions, set comparison, duplicate and null profiling, and layer-to-layer reconciliation queries written unaided
• Hands-on Microsoft Fabric and/or Power BI experience across lake houses, notebooks, semantic models, and published reports in dev/test/prod work spaces
• Solid understanding of dimensional modeling - grain, surrogate vs. business keys, facts vs. dimensions, SCD basics
• Proven independent delivery in a small or single-QA environment with strong written and verbal communication
• Strict discipline with sensitive data - masked samples, no PII/PHI in test artifacts, no secrets in documents

• Fabric medallion delivery and Direct Lake semantic model experience
• Health plan, claims, pension, or retirement benefits data background
• PySpark or notebook-based test assertions, or experience with frameworks like Great Expectations
• Azure Dev Ops or Git with deployment pipelines
• Legacy on-premises SQL Server source experience