Senior Software Engineer - ETL + AT Tester

Job Description – Senior QA Engineer (ETL + AI/ML Testing)

Experience: 10+ years
Role: Senior Quality Assurance Engineer
Location: —
Type: Full-time

About the Role

We are looking for an experienced Senior QA Engineer with strong expertise in ETL/data validation and hands-on experience in testing AI/ML pipelines. The ideal candidate should be able to design test strategies, lead QA activities, validate large datasets, and ensure the correctness and quality of ML models and data workflows.

Key Responsibilities

• Lead and own QA activities across ETL pipelines, data flows, and AI/ML model life cycles.
• Design test strategies, test plans, and test cases for data ingestion, transformation, and loading processes.
• Validate data integrity, data mapping, data quality, and performance of ETL workflows.
• Perform AI/ML testing including:
• model input–output validation
• feature-level testing
• model performance verification
• bias, accuracy, and drift checks
• Work closely with Data Engineers, ML Engineers, and Product teams to understand requirements.
• Automate test cases for ETL and ML workflows where possible.
• Identify defects, perform root-cause analysis, and ensure issues are resolved on time.
• Guide junior QA members and help set QA best practices.

Required Skills

• 10+ years of experience in QA, with at least 5+ years in ETL/Data Testing.
• Strong SQL skills for data validation (joins, aggregations, data profiling).
• Experience with ETL tools such as Informatica, Talend, SSIS, Databricks, Airflow, Glue, or similar.
• Good understanding of data warehousing concepts (SCD, fact/dimension tables).
• Hands-on experience testing AI/ML models, pipelines, or data features.
• Knowledge of Python for test automation and ML validation (preferred).
• Experience working with big data platforms (Hive, Spark, Databricks) is a plus.
• Familiarity with MLOps, model monitoring, or feature stores is an advantage.
• Strong analytical, communication, and documentation skills.

Nice-to-Have

• Experience with cloud platforms (AWS, Azure, GCP).
• Exposure to CI/CD pipelines for test automation.
• Understanding of model explainability tools and ML evaluation metrics.

Education

• Bachelor’s or Master’s degree in Engineering, Computer Science, or related field.