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Job Description
Structured overview of role & requirementsAbout This Role
Design and execute comprehensive data quality testing strategies for complex ETL/ELT data pipelines using platforms like Databricks and Snowflake.
Develop and maintain automated data validation frameworks leveraging SQL, Python, and test automation integrated into CI/CD pipelines.
Investigate data discrepancies through root cause analysis and collaborate with engineering teams to ensure data accuracy, integrity, and reliability.
Minimum Requirements
Minimum 5 years of professional experience in data quality engineering or related roles.
Strong expertise in SQL (complex joins, CTEs, window functions) and Python scripting for data validation and automation.
Hands-on experience with ETL/ELT testing, source-to-target validation, and modern data platforms such as Databricks and Snowflake.
Familiarity with CI/CD tools like Git, Jenkins, Azure DevOps, or equivalents and test frameworks such as PyTest.
Ideal Candidate Profile
Experience working with large-scale data processing using Spark, Delta Lake, and Medallion Architecture indicating comfort in modern cloud data environments.
Proven ability to build reusable, configuration-driven test automation frameworks integrated into workflows or orchestration tools like Apache Airflow or Databricks Workflows.
Prior exposure to AI-assisted testing, generative AI, LLMs, and RAG pipeline validations to add value in emerging data quality scenarios.