• Create analyses and reports to empower teams across the organization • Optimize and maintain data models that power analyses and visualizations • Transform raw data into clear, actionable insights that drive decisions • Partner with stakeholders to define questions and communicate findings • Build analytical models and dashboards that track key performance metrics • Establish and enforce data governance standards • Identify recurring questions or points of confusion in reporting

• Develop and maintain reporting and data visualizations in Looker Studio and Power BI • Oversee product analytics data infrastructure • Define, document, and maintain data layer requirements • Serve as a subject matter expert for analytics platforms • Ensure privacy and compliance in product analytics • Enable data democratization and adoption by training cross-functional partners • Support insight generation by collaborating with teams

The Role: Our objective is to reshape the value chain for our Members and Insurers using data driven insights. Our success will be based on the value data creates for our Members, risk capital providers, other suppliers and ourselves. This role is part of a newly created and fast-growing division. The Data Office combines 3 teams; Data Products, Data Quality and Data Management. Today, the Data Quality team consists of the Data Quality Lead and a Data Quality Support Analyst. The Data Quality Rules Analyst is responsible for front-line ownership of the validation lifecycle, translating Data Quality intent and standards into clear, consistent, and scalable deterministic controls across ingestion, data products, and systems. The role focuses on rule specification, catalogue governance, control behaviour, and tuning - ensuring that large volumes of data quality controls are well-structured, interpretable, and maintainable as the platform scales. Key Responsibilities Validation Intake & Front-Line Support - Act as the first point of contact for: - - New validation requests - Validation support requests (e.g. overrides, unexpected behaviour, suspected defects) - Clarify intent, scope, expected behaviour and enforcement with Data Owners and Product. - Determine whether requests can be handled within existing standards or require escalation. - Escalate to the Data Quality Lead only where intent, appetite or priority is unclear or challenged. Validation Rule Specification - Translate validation requirements into clear, build-ready rule specifications, including: - - rule intent and business description - severity and enforcement level - thresholds and tolerances - expected behaviour and override guidance - Define deterministic data quality controls across: - - ingestion validations - data product and cross-system controls - system- and application-level checks - Ensure rules are unambiguous, testable, and monitorable, resolving specification-level ambiguity independently where possible. Validation Catalogue Ownership - Own and maintain the validation catalogue content as a governed artefact, including: - - rule metadata and descriptions - ownership mapping - lifecycle status (proposed, active, noisy, deprecated) - linkage to datasets and Atlan metadata - Ensure catalogue hygiene and consistency as rule volumes scale. - Rationalise validation inventories by: - - identifying duplicate or overlapping rules - consolidating low-value checks - recommending retirement of obsolete or ineffective controls Control Monitoring, Interpretation & Tuning - Monitor rule behaviour, including: - - failure rates - override frequency - noise patterns - Interpret control behaviour and form hypotheses about root cause. - Propose bounded refinements to thresholds or logic where evidence is clear. - Escalate to the Data Quality Lead where changes affect agreed Data Quality appetite or enforcement posture. Anomaly Detection & RCA Support - Perform first-pass interpretation of anomalies and outliers. - Analyse data and control behaviour to support Root Cause Analysis (RCA). - Provide evidence-based recommendations to inform DQ Lead decisions. - Support Data Owners and Product during RCA with technical and analytical insight. Must-Have Experience · 3–6 years’ experience in data quality, data governance, analytics engineering, or data operations roles. · Experience working with large, complex datasets with high volumes of data quality validations. · Strong analytical mindset, able to interpret data behaviour, trends, distributions, outliers and cross-dataset consistency. · Proven experience interpreting data quality issues at scale and forming evidence-based recommendations on rule refinement, thresholds, and upstream process improvements · Experience defining and maintaining deterministic data quality controls, including clear rule intent, severity, thresholds and expected behaviour. · Validation catalogue experience, including maintaining rule metadata, ownership and lifecycle status. · Working knowledge of SQL sufficient to interrogate datasets independently, explore data distributions, validate assumptions behind proposed controls, and investigate unexpected validation behaviour · Experience supporting Root Cause Analysis (RCA) through data analysis and evidence gathering. · Ability to operate effectively in a maturing data quality environment, applying defined standards and guardrails while tooling and processes continue to evolve. · Strong collaboration skills, working closely with Product, Technology and Data teams to ensure controls are implemented as specified.

• Collect, clean, and validate large sets of program-related data from multiple sources. • Analyze data to identify trends, patterns, and areas of concern or opportunity for the organization. • Develop, maintain, and update dashboards, reports, and visualizations to support program management and reporting needs. • Interpret data findings and present actionable recommendations to stakeholders and management. • Ensure the accuracy, security, and integrity of all data and analyses. • Prepare routine and ad hoc data reports for internal and external use, ensuring compliance with SSA, privacy, and other regulatory standards. • Collaborate with cross-functional teams to define data needs, support project objectives, and streamline data collection processes. • Use statistical and analytical tools to perform quantitative and qualitative analyses. • Document methodologies, data definitions, and processes clearly for future reference and audit purposes. • Support data migration, data integration, and quality assurance initiatives as needed. • Stay current with best practices in data analytics, visualization, and privacy/security requirements.