QA Automation Lead (Data Engineering)
IndiaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 7–10 years
- Required Skills
- PythonSQLSnowflakeCI/CDBigQueryRedshift
Requirements
- 7–10 years of experience in software quality assurance with strong exposure to data engineering environments.
- Strong proficiency in SQL for complex data validation, reconciliation, and root cause analysis.
- Solid understanding of data engineering concepts including ETL/ELT pipelines, data warehouses, and distributed data systems.
- Hands-on experience with Python for test automation, debugging, and data pipeline validation.
- Proven experience designing and implementing QA automation frameworks integrated with CI/CD pipelines.
- Experience owning release cycles, including testing strategy, execution, and production readiness validation.
- Familiarity with cloud data platforms such as Snowflake, BigQuery, or Redshift.
- Strong analytical thinking, attention to detail, and problem-solving skills.
- Excellent communication skills and ability to work independently in Agile, fast-paced environments.
- Proactive use of AI tools for automation, debugging, and productivity enhancement in QA workflows.
Responsibilities
- Define and drive the end-to-end data QA and automation strategy for data engineering projects, ensuring data integrity and reliability across platforms.
- Design, develop, and execute advanced test cases for backend data systems, including ETL/ELT pipelines, batch, and streaming architectures.
- Build and maintain scalable QA automation frameworks and data validation suites integrated into CI/CD pipelines.
- Leverage AI-powered development tools to accelerate test creation, debugging, and optimization of data validation workflows.
- Ensure robust data governance, quality standards, and best practices across engineering teams.
- Partner with data engineering and product teams to define release readiness criteria and support Go/No-Go decisions.
- Drive automation-first testing approaches to reduce manual reconciliation and improve efficiency.
- Establish monitoring, logging, and observability standards for data quality across pipelines.
- Lead and mentor QA engineers while promoting technical excellence and continuous improvement.
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