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.