Data & AI/ML QA Engineer
First seen by Alion on Oct 9, 2026.
Role : Data Quality Engineer
Experience : 3 - 7 Years
Location : Bengaluru
Notice Period : Immediate Joiners Preferred
Job Description :
We are looking for an experienced Data Quality & AI Validation Engineer with 3 - 7 years of experience in data testing, SQL-based validation, data pipeline testing, and AI/ML output verification. The ideal candidate will have strong hands-on expertise in SQL, Snowflake, and dbt testing, with the ability to independently validate analytical findings, reconcile data against source systems, and ensure the accuracy, consistency, and traceability of data-driven insights.
The role focuses on validating data pipelines, analytics outputs, business rules, and AI-generated recommendations rather than traditional application or UI testing. Experience evaluating AI/ML or LLM-generated outputs against ground truth is highly desirable.
Key Responsibilities :
- Independently validate analytical findings, rule dispositions, and data-driven outputs against source data.
- Write and execute complex SQL queries to verify analysis results and reconcile outputs using Snowflake.
- Perform hands-on dbt testing, including schema tests, data tests, assertions, and custom validation rules.
- Implement data-quality checks for uniqueness, not-null constraints, referential integrity, accepted values, and other business rules.
- Identify and investigate duplicate records, expired or invalid records, data inconsistencies, and false positives in detection logic.
- Perform data reconciliation by validating row counts, edge cases, source-to-target mappings, and output consistency.
- Test data pipelines, ETL/ELT transformations, and analytics outputs to ensure accuracy, completeness, and reliability.
- Build validation checks into dbt pipelines to ensure findings are reproducible and traceable to source data.
- Validate AI/ML and LLM-generated outputs by comparing sampled results against ground truth and expected outcomes.
- Evaluate AI-agent recommendations for accuracy, consistency, reliability, and potential failure patterns across repeated runs.
- Identify, document, investigate, and report data-quality issues, incorrect findings, and inconsistencies.
- Maintain data lineage, traceability, and validation evidence to support auditability and reproducibility.
- Collaborate with Data Engineers, Analytics Engineers, Data Scientists, and AI/ML teams to improve data quality and validation processes.
Required Skills :
- 3 - 7 years of experience in Data Testing, Data Quality Engineering, ETL/ELT Testing, or Analytics Validation.
- Strong SQL skills, with the ability to independently write queries against Snowflake and validate analytical findings.
- Hands-on experience with dbt testing, including schema tests, data tests, assertions, and custom data-quality checks.
- Strong understanding of data-quality validation, including duplicate detection, invalid or expired records, inconsistencies, and false-positive identification.
- Experience in data reconciliation, including row-count verification, source-to-target validation, edge-case testing, and output consistency checks.
- Experience testing data pipelines, data transformations, and analytical outputs rather than focusing on application or UI testing.
- Ability to independently investigate data discrepancies and verify that findings are correct, reproducible, and supported by source data.
Skills
Data Quality, SQL, Artificial Intelligence, Machine Learning, Snowflake DB, Data Build Tool, Data Validation, ETL/Datawarehouse Testing, Database Testing, AI/ML Testing, Testing