This role is responsible for ensuring the accuracy, reliability, and quality of enterprise ETL pipelines and AI-ready datasets. The individual will support analytics, business intelligence, and AI/ML initiatives by validating data pipelines, improving data quality frameworks, and enabling trustworthy model inputs.

Job Responsibilities

• Lead ETL and data quality testing initiatives across multiple data platforms
• Validate data pipelines supporting AI/ML models and analytics dashboards
• Implement AI-assisted testing techniques (anomaly detection, rule discovery)
• Develop reusable SQL/Python-based frameworks for automated data validation
• Review training datasets for consistency, completeness, and data integrity
• Identify and resolve data quality issues across batch and incremental loads
• Partner with data engineers and AI teams to ensure reliability of model inputs
• Ensure adherence to data governance, QA standards, and best practices
• Support Agile delivery through CI/CD-aligned data testing processes

Job Requirement

Experience:

• 6–10 years in ETL/Data Warehouse testing and data quality engineering
• Strong SQL expertise (joins, aggregations, subqueries)
• Solid understanding of data warehousing concepts (fact/dimension models, star schema)
• Hands-on experience in ETL testing tools (Informatica, Talend, DataStage, ADF, etc.)
• Experience with databases such as Oracle, SQL Server, Snowflake, Teradata, PostgreSQL
• Exposure to AI/ML data pipelines and feature engineering validation
• Familiarity with data observability or AI-enabled QA tools
• Working knowledge of Agile and CI/CD practices

Good to Have:

• Experience with cloud platforms (AWS, Azure, GCP, Snowflake)
• Basic Python or shell scripting for data validation
• Exposure to BI tools (Power BI, Tableau) validation
• Experience in Agile/Scrum environments

Education:

• Bachelor’s degree in Computer Science, IT, Engineering, or equivalent