Data Quality Analyst - OutsideIR - Insurance in London - Oliver James

he Senior Data Quality Analyst plays a critical role in shaping and governing data quality across a complex, data-driven insurance environment. Sitting within a growing Data Office, this role acts as the primary decision and interpretation layer for data quality controls-ensuring validations are meaningful, prioritised, and drive the right outcomes.

he Senior Data Quality Analyst plays a critical role in shaping and governing data quality across a complex, data-driven insurance environment. Sitting within a growing Data Office, this role acts as the primary decision and interpretation layer for data quality controls-ensuring validations are meaningful, prioritised, and drive the right outcomes.

The role focuses on reducing noise, improving control effectiveness, leading root cause analysis, and laying the foundations for data quality scoring, anomaly detection, and prevention, while working closely with data owners, product, and technology teams to ensure data quality aligns with business appetite and delivers real value.

Must-Have Experience

• 5-8+ years' experience in data quality, data governance, analytics, risk/control or data operations roles.
• Strong insurance domain experience - specialty insurance strongly preferred, P&C insurance as a minimum - with hands-on exposure to underwriting, exposure or risk data.
• Experience working with large, complex datasets where validation volume is high.
• Strong understanding of insurance concepts
• Proven experience interpreting data quality issues at scale and making judgement calls on rule vs education vs process fix and signal vs noise.
• Demonstrated ability to lead Root Cause Analysis (RCA) and challenge upstream processes constructively.
• Strong analytical mindset, comfortable reasoning about data behaviour and trends, distributions, outliers, and anomalies and aggregate and cross-dataset consistency.
• Working knowledge of SQL and/or Python sufficient to query datasets independently, explore data distributions and patterns, validate assumptions behind proposed controls, articulate validation logic and thresholds clearly.
• Ability to work effectively before tooling is fully mature, shaping how DQ tools, scoring, and anomaly detection should be used rather than waiting for perfect solutions.