Data Analyst & Validation Engineer -Redshift, Lake & QA

Ful-time role only - Preferred only US Citizens or GC Holders

HCLTech is looking for a highly talented and self- motivated Data Analyst & Validation Engineer (ETL QA Focused). Only US Citizens or GC holders required

Full time role) to join it in advancing the technological world through innovation and creativity.

Must have Skills - 6-10 years Data Validation& Validation & AWS Data services (Redshift, Lake Formation, Athena, and S3-based data lakes), Python, ETL ELT concepts, QA methodology (Test planning & design, Defect lifecycled.

• 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

• Define acceptance criteria for each ETL pipeline and transformation step — establishing what "correct" means in measurable, testable terms
• Define Definition of Done (DoD) for all data deliverables — specifying when a pipeline output is considered production-ready
• Create and maintain data quality test plans covering functional correctness, edge cases, regression, and performance
• Design test cases for new transformations before they reach production (shift-left testing)
• Establish data quality SLAs — freshness, completeness, and accuracy thresholds
• Define entry and exit criteria for pipeline releases — what must pass before promoting changes
• 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 — who approves what before data reaches the UI layer
• Design the overall data testing strategy:
• Unit tests for individual transformation logic
• Integration tests for end-to-end pipeline correctness
• Regression tests for ongoing stability
• Smoke tests for post-deployment verification
• Negative tests for resilience (nulls, duplicates, out-of-range values, schema violations)
• Build reusable, parameterized validation patterns applicable across multiple pipelines
• Implement data contract validation — ensuring upstream sources meet agreed schemas and value constraints
• Integrate quality gates into CI/CD pipelines for automated release blocking
• Document expected behavior for each transformation (input, logic, expected output)
• Maintain data quality runbooks — investigation and resolution procedures for common issues
• Create traceability matrices linking business requirements to test cases to pipeline outputs
• Document known data limitations, assumptions, and technical debt
• Maintain a living catalog of data quality rules and their rationale

• Build data quality dashboards tracking pass/fail rates, trend analysis, and SLA compliance
• Configure alerting for data quality threshold breaches
• Track and report data quality metrics (DQ score, defect density, mean time to detect, mean time to resolve)
• Conduct data quality retrospectives — identifying gaps and improving coverage
• Work with product and business stakeholders to translate vague requirements into testable assertions
• Collaborate with data engineers on pipeline improvements driven by quality findings
• Support UI/frontend teams in verifying rendered data correctness

Advanced — window functions, CTEs, set comparisons, complex joins, data profiling queries

Hands-on experience querying and validating data in Amazon Redshift, AWS Lake Formation, Athena, and S3-based data lakes