Data Quality Improvement Manager

Data Quality Improvement Manager

Join to apply for the Data Quality Improvement Manager role at Falcon Smart IT (Falcon

Smart

IT)

Job Location:

London or Ipswich UK / Hybrid

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We recognizes that being a truly data-driven organisation is critical to our success. To enable this, we need to ensure we capture consistently high quality, actionable data relating to our clients in our source systems, to power analysis and decision‑making across all our key business functions (e.g., risk, underwriting, pricing, actuarial, finance, claims, operations, etc.)

The Data Quality & Culture team within the Innovation, Data & Analytics (IDA) division is focused on driving the data quality strategy at through 3 work streams:
Train
,
Heal
&
Prevent
.

• ‘Train’ – developing a data culture to understand the importance of data to our business, and the need to capture data “right first time” in our source systems to ensure data quality.
• ‘Heal’ – finding & fixing data errors in our systems after they occur with SQL data quality validation rules and Power BI dashboards to track quality levels and drive remediation efforts.
• ‘Prevent’ – working with Operations and IT teams, to implement “data quality by design” across various source systems & processes, to prevent manual data quality errors from occurring in the first place (e.g. working with IT to replace an optional free text field, with a mandatory drop down list of choices on Genius – a core policy administration system).

This role will be focused primarily on the ‘Prevent’ work stream.

• Demonstrate advanced SQL coding skills to interrogate diverse databases, enabling the identification and analysis of root causes of data quality issues.
• Ability to extract, join, and compare data records across multiple systems to support data integrity investigations.

• Effectively collaborate with cross‑functional stakeholders across departments (e.g., Underwriting, Actuarial, Claims, Operations) to understand data workflows, identify data quality challenges, and gather requirements for improvements.
• Foster effective relationships to facilitate information sharing and drive data quality initiatives.

• Map critical data flows throughout the insurance lifecycle (submission, quote, bind, policy,