Data Analyst Engineer (ETL QA Focused)

Role: Data Analyst Engineer (ETL QA Focused)
Duration: Contract
Location: Remote

Primary Skills: AWS Data services (Redshift, Lake Formation, Athena, and S3-based data lakes), Python, ETL ELT concepts, QA methodology (Test planning & design, Defect lifecycle management)

Secondary Skills: SQL, Data profiling, Git

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 - 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

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