Evnek - Data & AI/ML QA Engineer

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.