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Niche GCP/data QA skills reduce applicant density despite metro locations.
Transferable across industries but requires GCP and data-pipeline expertise, so medium sensitivity.
Explicit 5+ years plus mandatory GCP tooling, dbt, data-quality frameworks, and CI/CD imply high filter strictness.
Lead QA efforts for complex data engineering pipelines on Google Cloud Platform, ensuring data accuracy, reliability, and performance at scale.
Design and implement multi-layered testing strategies including integration, end-to-end, and data quality tests across GCP tools like dbt, Dataflow, Dataproc, BigQuery, AlloyDB, Cloud SQL, Cloud Composer, and Cloud Run.
Embed QA processes within CI/CD pipelines and provision reproducible test environments using IaC tools; mentor junior QA members and collaborate with data engineers to improve test effectiveness and data quality KPIs.
5+ years of experience in QA or software testing with a focus on data pipelines/data warehouses.
Proficient in complex SQL and data validation queries; strong experience with GCP data tools including dbt, BigQuery, Dataflow, Dataproc, Cloud Run, Spanner, AlloyDB, Cloud SQL.
Experience with data-quality frameworks like Great Expectations, Soda Core, dbt-utils, dbt-expectations and CI/CD automation tools (Jenkins, GitLab CI, GitHub Actions).
Prohibited visa sponsorship or transfer; notice period not explicitly mentioned in JD.
Experienced in defining testing methodologies and automating data quality validations across the full GCP data engineering stack in enterprise environments.
Skilled in scripting (Python, pytest) and familiar with observability tools (Cloud Logging, Elementary Data) with exposure to data governance, lineage, and compliance validation.
Able to lead and mentor QA teams with expertise in pipeline testing, database emulators, API testing with Postman, and CI/CD integration for continuous test automation.