Senior Test Lead

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 SLAs

• Define 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 SLAs

• Define 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 frameworks

ETL/ELT Concepts-Deep understanding of extraction, transformation, and loading patterns, including common failure modes

QA 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

Experience

• 6-10 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 UIs

• Familiarity 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/SLOs

• Knowledge 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

null