What are the responsibilities and job description for the Senior Test Lead position at HCLTech?
King, Washington
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
Maximum Salary (US): 148000
Minimum Salary (US): 78000
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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
Maximum Salary (US): 148000
Minimum Salary (US): 78000
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Benefits:
Health Insurance, Vacation & Paid Time Off, 401K PlanSalary : $148,000