Partnering with business stakeholders to understand business questions and deliver actionable insight.
Analysing key Group Wide metrics such as AUM, flows, adviser productivity, client segmentation, charging structures, and operational process performance.
Identifying and addressing data quality issues, working across the data lineage from source systems to dashboards.
Writing expert-level SQL for extracting, transforming, and validating data.
Proactively identifying performance bottlenecks, indexing and optimisation techniques.
Collaborating effectively with central data engineering teams to maintain reusable datasets within SQL Server and Azure Fabric.
Preparing and validating datasets used by Data Scientists for AI and ML solutions.
Applying data warehousing principles including dimensional modelling, SCDs, conformed dimensions, and KPI standardisation.
Helping shape curated, governed datasets in Data Lake suitable for advanced analytics and self‑service reporting.
Designing robust semantic models in Power BI using best‑practice star schema principles.
Using accessible and consistent UX design aligned with business standards.
Supporting the business with building self-service capability by developing understanding of how data works and the value add it provides to solving operational or client related problems.
Utilising Git for source control, including pull requests, naming conventions and peer review.
Participating in testing, reconciliation, validation, and release processes ensuring all analytical assets are secure, traceable, and reusable.
Effectively collaborating with Architects and Engineers to validate technical solutions.
Supporting the broader Titan team by taking on any other duties in line with your roles that may be reasonably requested of you.
Requirements
Strong SQL skills, including building complex views and stored procedures independently.
Solid data engineering experience, with good Git/source control practice and the ability to design simple, reliable solutions.
Advanced Power BI skills, including semantic model design, DAX, and creating high‑quality reports.
Good understanding of data warehousing principles and how they support analytics and MI.
In‑depth knowledge of UK Wealth or Asset Management, including key processes such as valuations, trades, holdings, onboarding, adviser charging, and portfolio/risk profiling.
Experience working with wealth platforms, CRM systems, and operational systems used in advice, DFM, or platform environments.
Strong understanding of regulatory and operational requirements, including FCA expectations for data quality, audit trails, MI consistency, and robust controls.