Senior Test Lead

Maximum Salary (US): 148000Minimum Salary (US): 78000Job SummaryData Validation & VerificationValidate transformation logic against business rules and documented specificationsPerform source-to-target data reconciliation — verifying completeness, accuracy, and consistencyIdentify data anomalies, silent failures, and drift in pipeline outputsBuild and maintain automated data validation suites that execute as part of pipeline runs Conduct periodic data audits beyond automated checks QA Process Definition & GovernanceDefine acceptance criteria for each ETL pipeline and transformation stepDefine Definition of Done (DoD)Create and maintain data quality test plans covering functional correctness, edge cases, regression, and performanceDesign test cases for new transformationsEstablish data quality SLAsDefine entry and exit criteria for pipeline releasesMaintain a defect taxonomy — categorizing data issues (schema drift, logic errors, source issues, timing issues) for root cause tracking and trend analysisDefine sign-off workflowsTest Strategy & Frameworks, Documentation & Traceability, Monitoring, Observability & Reporting, CollaborationKey ResponsibilitiesData Validation & VerificationValidate transformation logic against business rules and documented specificationsPerform source-to-target data reconciliation — verifying completeness, accuracy, and consistencyIdentify data anomalies, silent failures, and drift in pipeline outputsBuild and maintain automated data validation suites that execute as part of pipeline runs Conduct periodic data audits beyond automated checks QA Process Definition & GovernanceDefine acceptance criteria for each ETL pipeline and transformation stepDefine Definition of Done (DoD)Create and maintain data quality test plans covering functional correctness, edge cases, regression, and performanceDesign test cases for new transformationsEstablish data quality SLAsDefine entry and exit criteria for pipeline releasesMaintain a defect taxonomy — categorizing data issues (schema drift, logic errors, source issues, timing issues) for root cause tracking and trend analysisDefine sign-off workflowsTest Strategy & Frameworks, Documentation & Traceability, Monitoring, Observability & Reporting, CollaborationSkill RequirementsSQL-Advanced — window functions, CTEs, set comparisons, complex joins, data profiling queriesAWS Data Services-Hands-on experience querying and validating data in Amazon Redshift, AWS Lake Formation, Athena, and S3-based data lakesPython (or equivalent scripting) - Validation scripts, data comparison tools, automation frameworksETL/ELT Concepts-Deep understanding of extraction, transformation, and loading patterns, including common failure modesQA Methodology-Test planning, test case design, acceptance criteria definition, defect lifecycle managementData Profiling-Statistical profiling, distribution analysis, completeness and uniqueness checksValidation Frameworks-Hands-on experience with at least one: Great Expectations, dbt tests, Soda Core, or equivalent custom frameworksVersion Control-Git — managing test suites alongside pipeline codeExperience6-10 years of combined experience in data engineering, data QA, or analytics engineeringHas owned data quality for at least one production system end-to-end (not just contributed)Has defined acceptance criteria and quality gates that blocked defective releasesHas built automated validation suites that caught real production issuesComfortable reading and reasoning about pipeline code (transformation logic, orchestration DAGs)Experience working with curated/aggregated datasets that serve application UIsFamiliarity with AWS Glue, Redshift Spectrum, and AWS data pipeline services Preferred ExperienceExperience with BDD-style data testing (Given/When/Then for data transformations)CI/CD integration for data quality — automated gates in deployment pipelinesExperience defining and tracking data SLAs/SLOsKnowledge of regulatory or compliance data requirementsPerformance testing for pipelines — verifying latency and throughputExposure to chaos engineering for data — intentionally injecting bad data to test resilienceExperience with pipeline orchestration tools (Glue Orchestrator, Step Functions, Airflow)Experience with IAM permissions and Lake Formation access controls for data governanceOther Requirements null