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Tier-1 brand, remote role, mid-senior generalist title, and broad data+automation skillset increase candidate competition.
Requires specialized data engineering QA skills, moderately transferable across data-centric industries.
Explicit 7–10 years plus mandatory SQL, Python, data engineering and automation skills make filtering strict.
Own end-to-end data testing strategy ensuring integrity, accuracy, and performance of data pipelines and analytical platforms.
Lead design and execution of complex backend and pipeline test cases focusing on data validation in distributed systems and asynchronous processing.
Partner with data engineering and product teams to define automation-first quality roadmap and oversee release readiness for data platform features.
7–10 years of experience in Software Quality Assurance with leadership in full software and data lifecycle testing.
Bachelor's degree in Computer Science, Data Engineering, or equivalent experience.
Strong proficiency in SQL and Python with the ability to write complex queries and debug data pipeline code.
Knowledge of data engineering concepts including ETL/ELT workflows and data warehouse architecture (e.g., Redshift, Snowflake, BigQuery).
Experienced leader in QA automation with track record of building scalable test frameworks integrated with CI/CD pipelines.
Familiar with AI-powered testing tools (e.g., Cursor, Augment, Gemini) for accelerating test development and debugging.
Able to manage complex release cycles for data features, ensuring high data quality and governance standards in fast-paced Agile teams.