Data Quality Analyst (Life and Pension Insurance) at Tenth Revolution Group, London, £450-£500 per day

Life & Pensions Insurance | Outside IR35 | 3-6 Month Contract | £450- £500 per Day | Mostly Remote

We are supporting a major data transformation programme within the Life & Pensions insurance sector and are seeking an experienced Data Quality Analyst to join on an initial 3-6 month Outside IR35 contract.

This role will focus on improving the quality, accuracy, completeness and consistency of business-critical insurance and pensions data. You will work closely with business stakeholders, SMEs and technical teams to identify data quality issues, define validation requirements and support the delivery of data improvement initiatives across complex policy, member and scheme datasets.

The successful candidate will bring strong experience in data quality analysis, data validation and data profiling, alongside a solid understanding of insurance data. Experience within Life & Pensions, Pension Administration, Annuities or broader Insurance environments is highly desirable. The role will also involve working closely with cloud-based data platforms, so experience with AWS and Databricks is essential.

This is a predominantly remote opportunity, with occasional travel required for workshops and stakeholder meetings.

• Analyse large and complex datasets to identify data quality issues, inconsistencies, anomalies and data gaps.
• Perform data profiling activities to assess data completeness, accuracy, consistency and integrity.
• Define, document and maintain data quality rules, validation criteria and acceptance standards.
• Work closely with business SMEs to understand data requirements and translate them into clear specifications.
• Identify root causes of data quality issues and support remediation activities.
• Assess data quality across multiple source systems and datasets.
• Create and maintain data quality documentation, rule catalogues and reporting requirements.
• Support the implementation of data quality controls and validation processes.
• Work with Data Engineers and technical teams to ensure data quality requirements are correctly understood and delivered.
• Investigate exceptions, reconciliation issues and validation failures.
• Support testing activities and validate that implemented solutions meet business requirements.
• Develop data quality metrics and support ongoing monitoring and reporting.
• Contribute to best practice around data governance, data standards and quality management.
• Analyse and validate data held within AWS and Databricks environments, supporting cloud-based data quality and transformation initiatives.

• Proven experience working as a Data Quality Analyst, Data Analyst or similar role.
• Strong experience delivering data quality, data validation and data cleansing initiatives.
• Ability to analyse complex datasets and identify data issues, trends and anomalies.
• Strong SQL skills for data interrogation, analysis and validation.
• Experience defining and documenting business rules, data quality controls and validation requirements.
• Strong data profiling and root cause analysis experience.
• Experience working closely with business stakeholders and subject matter experts.
• Strong documentation skills, including requirements gathering and acceptance criteria definition.
• Experience supporting testing, defect investigation and data remediation activities.
• Excellent communication and stakeholder management skills.
• Experience working with AWS data services and Databricks for data analysis, profiling and validation.

• Experience within Life & Pensions Insurance.
• Knowledge of Defined Benefit (DB) or Defined Contribution (DC) pension schemes.
• Experience working with policy, member, scheme or actuarial data.
• Understanding of pension administration processes.
• Experience supporting data migration, remediation or transformation programmes.
• Exposure to cloud-based data platforms such as AWS and Databricks.
• Experience with Power BI reporting and operational MI.
• Understanding of data governance, lineage and metadata management principles.