Our purpose is to give everyone real confidence to put their money to work. With a heritage dating back more than 175 years, we have a long history of innovation in savings and investments, combining asset management and insurance expertise to offer a wide range of solutions.
Our two distinct operating segments, Asset Management and Life, work together to provide access to balanced, long-term investment and savings solutions.
Through telling it like it is, owning it now, and moving it forward together with care and integrity; we are creating an exceptional place to work for exceptional talent.
We will consider flexible working arrangements for any of our roles and also offer work place accommodations to ensure you have what you need to effectively deliver in your role.
The Role
Working with in Life Data, MI and Analytics, part of the wider Data, MI and AI team, the Data & Visualisation Engineer is responsible for creating and turning data into decisions, engineering reliable datasets and creating clear business-oriented visualisations that support confident decision-making across the Life business.
Collaborating closely with business stakeholders, you will turn their needs into well-designed data models, semantic layers, shared data sets and repeatable data products and provide insight through reporting - making complex information understandable and actionable. You will own and deliver elements of our workstack, helping to translate complex and ambiguous requirements into clear delivery, helping the business use trusted and high-quality data that is delivered on time, to the right standard, and aligned to outcomes that matter most for customers and the business.
What You’ll Be Accountable For
Building usable Data
- Design, build and maintain ELT pipelines and curated data models aligned to best practice and enterprise strategy/policy.
- Produce well‑governed data products that include documentation and relevant data quality checks.
Bringing insight to Life
- Build clean, performant Power BI models and reports using best‑practice DAX, data modelling, and visual storytelling.
- Design self‑service–ready BI assets with intuitive navigation, drill paths, KPIs, and filters.
- Document metric definitions, business rules, and logic in clear, reusable formats.
Data Quality and Reliability
- Profile, validate, and reconcile source data; embed data quality rules and monitoring.
- Investigate issues using structured root‑cause analysis and partner with SMEs to resolve.
- Maintain documentation, lineage and controls as part of the delivery lifecycle.
- Ensure semantic models and datasets are versioned, documented and released in line with team and enterprise standards.
Delivery excellence
- Contribute to backlog refinement, estimation and planning.
- Deliver iteratively with quick feedback loops.
- Collaborate closely with stakeholders and SMEs across the business, technology partners, and the wider team.
- Communicate impacts, risks, and trade‑offs clearly and early.
Stakeholder Management And Requirements Gathering
- Partner with business teams (Operations, Propositions, Advice, Distribution, Marketing, Finance and others) to understand desired outcomes, key decisions, and constraints.
- Lead discovery sessions to map current reporting, source systems, dependencies, and pain points.
- Run structured requirement gathering workshops, interviews, and data walkthroughs.
- Translate ambiguous requests into clear requirements (metrics, dimensions, logic, refresh cycles, SLAs).
- Challenge and refine requirements, reconciling conflicting definitions and negotiating pragmatic scope.
- Support and execute testing, incorporating feedback into iterative improvements.
What Success Looks Like
- Stakeholders regularly use your data products and/or reports to make timely, confident decisions.
- Semantic models and definitions are consistent across business areas and reduce rework.
- Data pipelines and visuals are reliable, performant, and easily maintainable.
- Data quality issues are identified, tracked, and resolved, with trends improving over time.
- You make complex concepts simple—your documentation enables others to self‑serve.
Essential
Skills, knowledge and experience
Stakeholder Engagement & Elicitation
- Skilled at understanding business context and converting it into structured data/BI requirements.
- Ability to reconcile conflicting definitions and drive alignment around a shared version of the truth.
- Experience facilitating workshops, walkthroughs, UAT sessions, and iterative review cycles.
- Capture functional and non-functional requirements including data latency, refresh cycles, dependencies, lineage and data sensitivity.
Data Modelling, Architecture and Semantics
- Hands‑on experience designing data models for the creation of data products and/or the delivery of analytics.
- Experience designing and maintaining shared datasets and semantic layers with versioning and ownership.
- Strong s