Connecting Odds
HCLTech

Senior Test Lead

HCLTech
King, Washington, United StatesPosted 24 days agoDiscoveredMatch locked

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

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

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

Not included in the source posting: about the role, what you'll do, benefits.

Skills

awsqaetlredshiftairflowci-cddbtgitiampythonsql

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