Data Quality Analyst - OutsideIR35 - Insurance

Data Quality Analyst - OutsideIR35 - Insurance

Posted 1 day ago by Oliver James

Negotiable
Outside
Undetermined
City Of London, England, United Kingdom

Summary: The Senior Data Quality Analyst is essential in managing and enhancing data quality within a complex insurance environment. This role involves making critical decisions regarding data quality controls and collaborating with various teams to ensure data quality aligns with business needs. The analyst will focus on improving control effectiveness and conducting root cause analysis while working with large datasets. Strong experience in the insurance domain, particularly in specialty insurance, is required for success in this position.

Key Responsibilities:

  • Shape and govern data quality across the insurance environment.
  • Act as the primary decision layer for data quality controls.
  • Ensure validations are meaningful and prioritized.
  • Reduce noise and improve control effectiveness.
  • Lead root cause analysis and challenge upstream processes.
  • Collaborate with data owners, product, and technology teams.
  • Lay foundations for data quality scoring and anomaly detection.
  • Work with large, complex datasets and validate assumptions.

Key Skills:

  • 5-8+ years' experience in data quality, governance, analytics, or data operations.
  • Strong insurance domain experience, preferably in specialty insurance.
  • Hands-on exposure to underwriting and risk data.
  • Experience with large datasets and high validation volume.
  • Strong analytical mindset and understanding of data behavior.
  • Working knowledge of SQL and/or Python for querying datasets.
  • Ability to work effectively with immature tooling.

Salary (Rate): undetermined

City: City Of London

Country: United Kingdom

Working Arrangements: undetermined

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

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.