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Bangalore mid-level role increases competition, but niche GCP/data QA skills reduce applicant pool.
Specialized GCP data pipeline and dbt tooling significantly limit transferable candidate backgrounds.
Explicit 5+ years plus mandatory GCP data stack and data-quality tooling makes filters strict.
Lead QA efforts for complex data engineering pipelines on Google Cloud Platform including tools like Dataflow, Dataproc, BigQuery, AlloyDB, Cloud Run, etc.
Design and implement multi-layered testing strategies: integration, end-to-end, and data quality tests covering pipelines, databases, and containerized services.
Embed QA within CI/CD pipelines, collaborate with data engineers to ensure testability, and mentor junior QA team members to improve data quality KPIs.
5+ years of experience in QA or software testing focused on data pipelines and data warehouses.
Proficiency in complex SQL and data validation queries along with strong experience in GCP data tools: dbt, BigQuery, Dataflow, Dataproc, Cloud Run, Spanner, AlloyDB, Cloud SQL.
Hands-on experience with data quality frameworks (Great Expectations, Soda Core, dbt-utils), CI/CD automation tools (Jenkins, GitLab CI, GitHub Actions), and scripting languages like Python and pytest.
Work Experience Required: 5+ years; Notice Period: Not explicitly mentioned; Visa sponsorship not available; Degree Requirements: Not explicitly mentioned.
Experienced in working with full GCP data engineering stack and capable of creating comprehensive, automated testing strategies covering pipelines and databases.
Able to lead QA initiatives including mentoring peers and continuously improving data quality metrics through collaboration and effective use of observability tools.
Qualified to work in India without visa sponsorship; has strong skills in SQL, GCP data tools, and automation emphasizing scalable and reproducible test environments.