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

Senior Quality Engineer

Key skills