About this role
Job Title: Senior Data Quality Engineer
Experience: 6–8 Years
Job Description:
• Own end-to-end data quality validation across ingestion, transformation, and consumption layers
• Design and maintain ETL automation frameworks using Python and Pytest
• Perform source-to-target validation, reconciliation, and anomaly detection
• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)
• Implement data quality rules such as null checks, schema validation, and integrity checks
• Use DataGaps (preferred) or similar tools for data quality monitoring
• Collaborate with data engineers, architects, and product teams to ensure quality
• Validate ETL workflows, scheduling, and failure handling mechanisms
• Execute SQL and Python-based validations for batch and real-time data
• Integrate automated data tests into CI/CD pipelines
• Track and report defects using Zephyr and Jira
• Ensure compliance with data governance and regulatory standards
• Mentor junior engineers and contribute to best practices and frameworks
Required Skills:
• Strong experience in data pipeline validation, ETL testing, and data quality engineering
• Proficiency in Python, PySpark, SQL, and Pytest
• Experience with Selenium and DataGaps frameworks
• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)
• Experience with AWS (Glue, Iceberg) and lakehouse architectures
• Familiarity with test data management techniques
• Experience with performance testing tools (JMeter)
• Experience with Zephyr test management tool
• Knowledge of Docker and Kubernetes
Preferred:
• Experience with healthcare or regulated datasets
• Experience validating APIs, Tableau reports, and UI
• Familiarity with Agile methodologies
Education:
• Bachelor’s degree in Computer Science, Engineering, or related field
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