Data Quality Improvement Manager

We recognise 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 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.

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

What you’ll be doing

• Data Analysis & SQL Proficiency - 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.
• Stakeholder Engagement & Communication - 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.
• Data Mapping & Process Improvement - Map critical data flows throughout the insurance lifecycle (submission, quote, bind, policy, claims) to identify process inefficiencies, duplication, and opportunities for quality enhancement. Analyse data journeys to recommend process redesigns that embed quality by design principles.
• Presentation & Storytelling - Develop impact presentations and communicate complex data insights clearly and persuasively to diverse audiences. Create compelling business cases and project plans that secure stakeholder buy-in and support for data quality initiatives.
• Cross-Functional Partnership - Build and maintain productive relationships with global technology teams, particularly source system delivery leads, to define system and process improvements. Collaborate on prioritising and implementing data quality enhancements within technology backlogs.
• Team Leadership & Coordination - Lead virtual, cross-disciplinary teams by coordinating activities across Transformation, Data Management, and Data Quality functions. Drive alignment and accountability among stakeholders to ensure timely delivery of data quality projects.
• Project & Budget Management - Plan, execute, and oversee projects from initiation to closure, ensuring delivery within scope, schedule, and budget constraints. Utilise project management methodologies to coordinate resources, track progress, and manage risks effectively.
• Progress Monitoring & Reporting - Advocate for systemic data quality improvements across the organisation by promoting best practices and system fixes. Provide regular operational updates and progress reports to senior leadership, including division and global data quality teams.
• Performance Measurement & Reporting - Develop metrics and KPIs to evaluate data quality improvements (e.g., system enhancements deployed, policies corrected). Collaborate with Business Intelligence teams to track benefits and continuously improve measurement frameworks.

What you’ll bring

We view individuals holistically through their People, Business, and Technical Skills. We are interested in what you bring, how you think, and your potential for growth. We value diverse perspectives, recognising that each person contributes uniquely to our team's success. We welcome candidates with relevant education and experience in a related field, as well as those with diverse educational backgrounds or equivalent experience.

Here are some of the key skills important for the role:

• PEOPLE Skills
• Customer Centricity: Passionate about improving data quality to support success, with a focus on understanding user needs through user-centered design thinking.
• Cross Functional Collaboration: Skilled in developing and maintaining broad stakeholder relationships across multiple functions and source systems. Effective networking and stakeholder engagement skills to facilitate collaboration.
• Analytical & Strategic Mindset: Demonstrates advanced quantitative data analysis skills, exploring raw data and deriving insights to inform strategic decisions.
• Emotional Intelligence: Effective in qualitative stakeholder interviews, understanding concerns, and building trust to identify systemic issues and opportunities.
• Resilience: Tenacious in overcoming delays, hurdles, and blockers to ensure timely delivery of projects and improvements.
• Growth Mindset: Curious to understand how data drives and eager to identify opportunities for continuous improvement.
• BUSINESS Skills
• Business & Insurance Acumen: Multiple years of experience across various business functions such as Risk, Underwriting, Pricing, Actuarial, Finance, Claims, and Operations. Familiarity with commercial insurance systems and processes.
• Business & Process Improvement: Knowledge of formal methodologies like Lean and Six Sigma (desirable) to optimise data capture processes and system design. Experience applying best practices in process improvement to drive data quality enhancements.
• Stakeholder Management: Effective negotiation and influencing skills to prioritise system enhancements with IT and other decision-makers. Ability to synthesise complex information into presentations that secure buy-in from senior stakeholders.
• Technical Skills
• Quantitative Data Analysis & SQL Expertise: Demonstrate advanced ability to explore and summarise complex raw data sets using SQL, extracting meaningful insights from data quality reports and dashboards to inform decision-making.
• Commercial Insurance Domain Knowledge: Possess multiple years of direct experience across various business functions such as Risk, Underwriting, Pricing, Actuarial, Finance, Claims, and Operations, with familiarity of key insurance systems (e.g., policy administration, claims management).
• Effective Communication & Presentation: Skilled in synthesising complex technical and business information into clear, compelling presentations using PowerPoint and in-person delivery, to engage and secure buy-in from senior stakeholders.
• Project Management & Process Improvement: Proven ability to plan, lead, and drive technology and process improvement initiatives, utilising virtual teams to implement sustainable data quality enhancements in data capture systems.
• Data-Driven Mindset & Curiosity: Passionate about leveraging data to support strategic goals, with a curiosity to understand how data influences business outcomes and identifying opportunities for enhancement.
• Negotiation & Influence: Adept at persuading key decision-makers, particularly in IT, to prioritise system enhancements that improve data capture and integrity.
• Process Improvement Methodologies: Knowledgeable in formal methodologies such as Lean and Six Sigma (desirable), applying best practices to optimise data capture processes and system design.
• User-Centered Design & Agile Practices: Familiar with user-centred design thinking, including Agile sprints, discovery interviews, and journey mapping, to understand and improve data capture from the user perspective.
• Data Management & Governance: Understanding of data warehousing, cloud-based services, and data governance and controls, supporting best practices in data quality management (certifications like CDMP are desirable but not mandatory).