Senior Test Lead

Maximum Salary (US): 148000

Minimum Salary (US): 78000

Job Summary Data Validation & Verification Validate transformation logic against business rules and documented specifications

Perform source-to-target data reconciliation — verifying completeness, accuracy, and consistency

Identify data anomalies, silent failures, and drift in pipeline outputs

Build and maintain automated data validation suites that execute as part of pipeline runs Conduct periodic data audits beyond automated checks QA Process Definition & Governance Define acceptance criteria for each ETL pipeline and transformation step Define Definition of Done (DoD)
Create and maintain data quality test plans covering functional correctness, edge cases, regression, and performance

Design test cases for new transformations

Establish data quality SLAsDefine entry and exit criteria for pipeline releases

Maintain a defect taxonomy — categorizing data issues (schema drift, logic errors, source issues, timing issues) for root cause tracking and trend analysis

Define sign-off workflows

Test Strategy & Frameworks, Documentation & Traceability, Monitoring, Observability & Reporting, Collaboration

Key Responsibilities Data Validation & Verification Validate transformation logic against business rules and documented specifications

Perform source-to-target data reconciliation — verifying completeness, accuracy, and consistency

Identify data anomalies, silent failures, and drift in pipeline outputs

Build and maintain automated data validation suites that execute as part of pipeline runs Conduct periodic data audits beyond automated checks QA Process Definition & Governance Define acceptance criteria for each ETL pipeline and transformation step Define Definition of Done (DoD)
Create and maintain data quality test plans covering functional correctness, edge cases, regression, and performance

Design test cases for new transformations

Establish data quality SLAsDefine entry and exit criteria for pipeline releases

Maintain a defect taxonomy — categorizing data issues (schema drift, logic errors, source issues, timing issues) for root cause tracking and trend analysis

Define sign-off workflows

Test Strategy & Frameworks, Documentation & Traceability, Monitoring, Observability & Reporting, Collaboration Skill Requirements SQL-Advanced — window functions, CTEs, set comparisons, complex joins, data profiling queries AWS Data Services-Hands-on experience querying and validating data in Amazon Redshift, AWS Lake Formation, Athena, and S3-based data lakes

Python (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 management

Data Profiling-Statistical profiling, distribution analysis, completeness and uniqueness checks

Validation Frameworks-Hands-on experience with at least one:
Great Expectations, dbt tests, Soda Core, or equivalent custom frameworks

Version Control-Git — managing test suites alongside pipeline code

Experience6-10 years of combined experience in data engineering, data QA, or analytics engineering

Has 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 releases

Has built automated validation suites that caught real production issues

Comfortable 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 Experience

Experience with BDD-style data testing (Given/When/Then for data transformations)
CI/CD integration for data quality — automated gates in deployment pipelines

Experience defining and tracking data SLAs/SLOsKnowledge of regulatory or compliance data requirements

Performance testing for pipelines — verifying latency and throughput

Exposure to chaos engineering for data — intentionally injecting bad data to test resilience

Experience with pipeline orchestration tools (Glue Orchestrator, Step Functions, Airflow)

Experience with IAM permissions and Lake Formation access controls for data governance

Other Requirements

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