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Metro locations and mid-level experience increase competition, though niche GCP data QA skills narrow the pool.
Specialized GCP data pipeline and dbt expertise limits cross-industry fit but remains moderately transferable.
Explicit 5+ years requirement and mandatory GCP data tooling skills make shortlisting strict.
Lead QA efforts for complex data engineering pipelines on Google Cloud Platform.
Design and implement multi-layered testing strategies including integration, end-to-end, and data quality tests across tools like dbt, Dataflow, BigQuery, Cloud Run, etc.
Embed QA in CI/CD pipelines and collaborate with data engineers and stakeholders to improve data quality KPIs and test effectiveness.
5+ years experience in QA or software testing focused on data pipelines/data warehouses.
Proficient in complex SQL and data validation queries.
Strong experience with GCP data tools: dbt, BigQuery, Dataflow, Dataproc, Cloud Run, Spanner, AlloyDB, Cloud SQL.
Experience in CI/CD automation using Jenkins, GitLab CI, or GitHub Actions.
Experienced in developing and maintaining testing methodologies specifically for the GCP data engineering stack.
Skilled in implementing data quality frameworks using tools like Great Expectations, Soda Core, and dbt-utils.
Capable of mentoring junior QA engineers and leading efforts to improve data quality and test effectiveness.