Data Quality Analyst ( ETLData Warehouse )

Job Summary

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:

• 610 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:

• Bachelors degree in Computer Science IT Engineering or equivalent