About this opportunity
HCLTech lists this Data Analyst & Validation Engineer -Redshift, Lake & QA opportunity in seattle, Washington. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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.
Key Responsibilities
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.
If you are interested in applying for this job, please make sure you meet the following requirements as listed below.
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 — 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
Test Strategy & Frameworks
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
Documentation & Traceability
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
Monitoring, Observability & Reporting
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
Collaboration
Participate in pipeline design reviews — flagging quality risks early in development
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
Required Qualifications
Technical Skills
Area
Requirement
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
Pay Range Minimum: 69000
Pay Range Maximum:
HCLTech is an equal opportunity employer, committed to providing equal employment opportunities to all applicants and employees regardless of race, religion, sex, color, age, national origin, pregnancy, sexual orientation, physical disability or genetic information, military or veteran status, or any other protected classification, in accordance with federal, state, and/or local law. Should any applicant have concerns about discrimination in the hiring process, they should provide a detailed report of those concerns to for investigation.
A candidate’s pay within the range will depend on their skills, experience, education, and other factors permitted by law. This role may also be eligible for performance-based bonuses subject to company policies. In addition, this role is eligible for the following benefits subject to company policies: medical, dental, vision, pharmacy, life, accidental death & dismemberment, and disability insurance; employee assistance program; 401(k) retirement plan; 10 days of paid time off per year (some positions are eligible for need-based leave with no designated number of leave days per year); and 10 paid holidays per year
How You’ll Grow
At HCLTech, we offer continuous opportunities for you to find your spark and grow with us. We want you to be happy and satisfied with your role and to really learn what type of work sparks your brilliance the best. Throughout your time with us, we offer transparent communication with senior-level employees, learning and career development programs at every level, and opportunities to experiment in different roles or even pivot industries. xhmxlyz We believe that you should be in control of your career with unlimited opportunities to find the role that fits you best.
Worksite address
seattle, WA, 98127, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.