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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Analyst Engineer - **Company:** Braintree - PayPal - **Location:** United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Airflow, Amazon S3, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Data Validation, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Transformation, Data Profiling, Database Testing, Identity and Access Management, Python (Programming Language), Reliability Engineering, Standard Sql, Test Case, Strategies of Testing, Data Processing, Performance Testing, Great Expectations (Foster Youth College-readiness and Support Program), Delivery Pipeline, Technical Debt, Git, Data Lakes, AWS Lake Formation, AWS Glue, AWS Data Analytics, Data Pipelines, Amazon Redshift - **Published:** October 2, 2026 - **Apply:** https://www.dice.com/job-detail/9b0ae707-7fd4-41f5-9aa3-21dc501b9851 ## About the Role 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 ## Description 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, · 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