Analyse and interrogate finance and operational data to answer client questions, producing clear, reliable reporting and analysis across client engagements.
Model and analyse finance datasets — including data for close, reporting, FP&A, and valuation — with clear lineage from raw inputs through to analysis-ready outputs.
Prepare and structure the data that supports applied AI and analytics delivery: retrieval sources, context datasets, and the inputs that keep analysis current.
Extract and combine data pragmatically from client systems (ERP, databases, SaaS, and legacy sources), working with the access control and sensitivity that client data demands.
Work to good analytical standards in day-to-day work: version control, reproducible and well-tested analysis, peer review, and clear documentation.
Build clear, reliable dashboards and reporting that stakeholders can trust, understand, and reuse.
Use AI-assisted tools (e.g. Claude, Cursor) to accelerate analysis, reporting, and insight work.
Translate ambiguous client questions into scoped, deliverable analysis on compressed timelines, then deliver it.
Collaborate closely with data engineers, data scientists, and non-technical client stakeholders, and present findings others can act on.

Several years of hands-on data analysis experience with evidence of delivered analysis and reporting that informed real decisions — not just proofs of concept.
Strong SQL and Python for data analysis, plus a BI/visualisation tool (e.g. Power BI, Tableau, or Looker), with clean, reproducible work.
Practical experience with modern data warehouse and analytics platforms (e.g. Snowflake, Databricks, BigQuery, or equivalent) and transformation tooling (e.g. dbt).
Comfortable working with messy, inconsistent real-world data and finding the signal in it.
Demonstrated experience preparing and structuring data for LLM-based or analytics systems — retrieval sources and structured context datasets — rather than theoretical familiarity.
Track record delivering under time constraints in fast-paced, high-ownership settings.
Experience delivering the analysis and reporting behind AI or analytics products, with a practical understanding of how data quality, freshness, and structure affect the reliability of insight.
Solid grasp of data modelling, finance metrics, and data governance.
Familiarity with version control (e.g. Git) and reproducible analysis workflows.
Working awareness of context window economics and when retrieval, long-context, or fine-tuning approaches change what the analysis needs to draw on.
Client-facing experience: able to engage stakeholders directly and explain trade-offs clearly.

Finance Data Analyst

Key skills