Senior Test Lead

Sapsf; United States; On-site; USD 120,000 - 160,000; Full time

Job summary

Sapsf is seeking a Senior Test Lead to own data quality across complex ETL pipelines in a US-based environment. You will design validation strategies, define acceptance criteria, and govern quality gates to prevent defects from reaching production.

You will build automated validation suites, review transformation logic, and collaborate with data engineers, analysts, and product teams to ensure accurate, timely data for dashboards and decisions.

Qualifications

• 6–10 years of combined experience in data engineering, data QA, or analytics engineering.
• Owned data quality for at least one production system end-to-end.
• Defined acceptance criteria and quality gates for releases.
• Built automated validation suites that caught production issues.
• Comfortable reading and reasoning about pipeline code.

Responsibilities

• 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 run as part of pipeline runs.
• Conduct periodic data audits beyond automated checks.

Skills

SQL-Advanced

AWS Data Services

Python scripting

ETL/ELT concepts

Data profiling

Validation frameworks

Git

Tools

Git

Airflow

dbt

Job description

Career Opportunities: Senior Test Lead (172698)

Requisition ID172698-Posted09/25/2026

• 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 criteriafor each ETL pipeline and transformation step
• Define Definition of Done (DoD)
• Create and maintain data quality test planscovering functional correctness, edge cases, regression, and performance
• Design test casesfor new transformations
• Define entry and exit criteriafor pipeline releases
• Maintain a defect taxonomy— categorizing data issues (schema drift, logic errors, source issues, timing issues) for root cause tracking and trend analysis

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

Key Responsibilities

• 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 criteriafor each ETL pipeline and transformation step
• Define Definition of Done (DoD)
• Create and maintain data quality test planscovering functional correctness, edge cases, regression, and performance
• Design test casesfor new transformations
• Define entry and exit criteriafor pipeline releases
• Maintain a defect taxonomy— categorizing data issues (schema drift, logic errors, source issues, timing issues) for root cause tracking and trend analysis

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

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
• Hasowneddata 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 withBDD-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