Senior Full Stack Data Quality Engineer
Assignment type: Outside IR35
The supplier will be engaged to provide specialist hands-on data quality engineering services to the Finance & Data Reporting Product Team across TMHCC’s modern data platform testing, data quality engineering, and test automation.
The engagement is focused on delivering agreed testing and automation outcomes across defined operational assurance priorities and initiative delivery outcomes. The supplier will validate data across the full data lifecycle, including ingestion, transformation, data lake, data warehouse, reporting and BI, APIs, metadata, lineage, and reconciliation.
Assignment Tasks / Deliverables
Full Stack Data Quality Engineering:
- Design and execute data quality testing across ingestion, transformation, warehouse, reporting/BI, APIs, metadata, lineage, and reconciliation layers.
- Validate Snowflake data lake and data warehouse outputs against source systems, business rules, transformation logic, and reporting requirements.
- Test ETL and ELT processes, dbt models, SQL transformations, data mappings, aggregations, reference data, and downstream Power BI outputs.
- Apply data quality checks covering accuracy, completeness, consistency, timeliness, validity, uniqueness, and reconciliation.
- Support root cause analysis of data defects and collaborate with engineering and business stakeholders to support timely resolution of agreed data quality findings.
- Provide clear test evidence, defect analysis, risk commentary, and release readiness input for product delivery decisions.
Automation Framework and Test Harness Maturity:
- Maintain and improve the existing dbt Core/dbt Cloud test harness for Snowflake-based data validation.
- Extend reusable automated test coverage for ingestion checks, transformation validation, regression testing, reconciliation, and reporting validation.
- Improve the test harness architecture so that it is easier to maintain, extend, and reuse across agreed assurance activities and defined initiative delivery outcomes.
- Build automated checks and supporting utilities using dbt, SQL, Python, GitHub, Azure DevOps, and relevant cloud data platform tooling.
- Integrate automated tests into CI/CD workflows to enable earlier detection of data quality issues.
- Create practical reusable assets such as test patterns, dbt macros, validation templates, regression packs, and framework documentation.
- Identify manual testing activities that can be automated to improve speed, repeatability, and confidence.
Legacy and Modern Data Platform Assurance:
- Deliver testing across the FDR transitional estate, including legacy SQL Data Warehouse and Snowflake modern data platform components, where included within agreed scope.
- Provide reconciliation and regression testing where data flows, reports, or downstream products are impacted by ERS migration activity.
- Assess risks arising from coexistence of legacy and modern platforms, including source mapping, transformation differences, reporting impacts, and cutover-related validation.
- Collaborate with FDR stakeholders and wider programme teams to understand dependencies, testing scope, acceptance criteria, and data quality implications.
- Ensure test approaches reflect both current-state operational needs and future-state platform direction
Product Delivery Leadership & Stakeholder Engagement:
- Collaborate with the Engineering Delivery Lead, Product Owner, Business Analysts, Developers, offshore partners, and Quality Engineering Practice as required to agree scope, dependencies, risks, and acceptance criteria.
- Translate business rules and data requirements into testable validation logic and automated checks.
- Support agreed operational assurance priorities and initiative delivery outcomes by applying risk-based testing and prioritising effort based on business impact.
- Participate in relevant delivery ceremonies where required to agree scope, dependencies, data quality risks, defect triage, release readiness, and delivery governance input.
- Provide concise reporting on test progress, automation coverage, defects, data quality risks, and delivery blockers.
- Work pragmatically with the evolving FDR operating model, supporting improved efficiency through stronger engineering-led testing practices and reusable deliverables.
Quality Engineering Maturity:
- Share knowledge and practical guidance on dbt testing, Snowflake validation, reconciliation, and automated data quality checks.
- Help improve testing standards, reusable patterns, and quality metrics within the Finance & Data Reporting Product Team.
- Enable client teams to understand, use, and extend the test harness through reusable guidance, documentation, and knowledge transfer.
- Contribute to quality measures such as test coverage, automation coverage, defect leakage, regression effectiveness, and data quality trends.
- Leave behind maintainable documentation, reusable patterns, and clear handover material to support continuity beyond the contract period.
