Key Responsibilities:

Data Validation & QA:

Write advanced SQL to validate transformations, joins, aggregations, and business rules across datasets and features.

Create test plans and acceptance criteria for new data products, features, and model outputs.

Automated Testing & Quality Gates:

Implement automated data tests (schema, freshness, distribution, reconciliation) using Python and/or data quality frameworks.

Integrate QA checks into CI/CD pipelines and enforce release quality gates.

Monitoring & Issue Management:

Set up monitoring for data anomalies and pipeline failures; triage issues and drive root-cause analysis.

Partner with data engineering and ML Ops to improve observability and reduce recurrence.

Documentation & Governance:

Document test coverage, known limitations, and lineage; support audits and compliance requirements.

Promote best practices for data definitions and metric consistency.

Technical Qualifications Needed:

Education:

Bachelors or Masters degree in Computer Science, Engineering, Statistics, or a related field.

Mandatory Required skills:

5+ years of experience in SQL engineering, analytics QA, or data quality roles.

Expert SQL skills including window functions, complex joins, and performance tuning.

Experience with automated testing (Python/pytest) and data quality approaches.

Familiarity with lake house/warehouse platforms (Azure SQL/Synapse/Databricks/Snowflake).

Strong problem-solving and communication skills; ability to work across technical and business teams.

Desired Skills:

Experience with Great Expectations, dbt tests, or similar quality frameworks.

Knowledge of ML workflows and validation of model outputs (stability, drift, bias indicators).

Experience building dashboards for data health and QA coverage.

Certifications

Relevant certifications (e.g., Azure Data Fundamentals, ISTQB) preferred.

Senior Quality Engineer