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About the Role

This role focuses on providing specialist hands-on data quality engineering services to the Finance & Data Reporting Product Team, concentrating on testing, data quality engineering, and test automation within TMHCC’s modern data platform. The engagement aims to deliver agreed testing and automation outcomes across operational assurance priorities and initiative delivery.

Responsibilities

• 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 for timely resolution.
• Provide clear test evidence, defect analysis, risk commentary, and release readiness input.
• 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 for easier maintenance, extension, and reuse.
• 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 for earlier detection of data quality issues.
• Create reusable assets such as test patterns, dbt macros, validation templates, regression packs, and framework documentation.
• Identify manual testing activities that can be automated.
• Deliver testing across the FDR transitional estate, including legacy SQL Data Warehouse and Snowflake modern data platform components.
• Provide reconciliation and regression testing where data flows, reports, or downstream products are impacted by ERS migration activity.
• Assess risks arising from the coexistence of legacy and modern platforms.
• Collaborate with FDR stakeholders and wider programme teams to understand dependencies, testing scope, acceptance criteria, and data quality implications.
• Ensure test approaches reflect current-state operational needs and future-state platform direction.
• Collaborate with the Engineering Delivery Lead, Product Owner, Business Analysts, Developers, offshore partners, and Quality Engineering Practice 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 prioritizing effort based on business impact.
• Participate in relevant delivery ceremonies 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.
• 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.

Requirements

• Experience with data quality engineering services.
• Experience with modern data platform testing.
• Experience with test automation.
• Experience validating data across the full data lifecycle, including ingestion, transformation, data lake, data warehouse, reporting and BI, APIs, metadata, lineage, and reconciliation.
• Experience with Snowflake data lake and data warehouse.
• Experience testing ETL and ELT processes, dbt models, SQL transformations, data mappings, aggregations, reference data, and downstream Power BI outputs.
• Experience applying data quality checks covering accuracy, completeness, consistency, timeliness, validity, uniqueness, and reconciliation.
• Experience supporting root cause analysis of data defects.
• Experience collaborating with engineering and business stakeholders.
• Experience providing test evidence, defect analysis, risk commentary, and release readiness input.
• Experience maintaining and improving dbt Core/dbt Cloud test harness.
• Experience extending automated test coverage for various data validation aspects.
• Experience improving test harness architecture.
• Experience building automated checks and supporting utilities using dbt, SQL, Python, GitHub, Azure DevOps, and cloud data platform tooling.
• Experience integrating automated tests into CI/CD workflows.
• Experience creating reusable assets such as test patterns, dbt macros, validation templates, regression packs, and framework documentation.
• Experience identifying manual testing activities for automation.
• Experience delivering testing across legacy and modern data platforms.
• Experience providing reconciliation and regression testing.
• Experience assessing risks arising from platform coexistence.
• Experience collaborating with stakeholders to understand dependencies, scope, and acceptance criteria.
• Experience translating business rules and data requirements into testable validation logic.
• Experience applying risk-based testing and prioritizing effort.
• Experience participating in delivery ceremonies.
• Experience providing reporting on test progress, automation coverage, defects, and risks.
• Experience working with evolving operating models.
• Experience sharing knowledge on dbt testing, Snowflake validation, reconciliation, and automated data quality checks.
• Experience improving testing standards, reusable patterns, and quality metrics.
• Experience enabling client teams through knowledge transfer.
• Experience contributing to quality measures.
• Experience leaving behind maintainable documentation and reusable patterns.

Skills

• Data Quality Engineering
• Data Platform Testing
• Test Automation
• Snowflake
• dbt Core
• dbt Cloud
• SQL
• Python
• GitHub
• Azure DevOps
• ETL
• ELT
• Power BI
• CI/CD
• Reconciliation
• Regression Testing
• Data Validation
• Data Lifecycle Management
• API Testing
• Metadata Management
• Data Lineage
• Root Cause Analysis
• Risk Assessment
• Reporting

Location

• Remote

Work Type

• Outside IR35
• Contract

Experience Level

• Senior

About the Company

• At Tokio Marine HCC, we pride ourselves on hiring the smartest, most conscientious people, who want to make a difference no matter their background.
• We provide the support and trust needed for our employees.
• We are always looking for curious, creative transformative thinkers who want to change the status quo and have a passion for doing the right thing.