Financial Data Analyst

Technical Financial Data Analyst

About the role

We are hiring a Technical Financial Data Analyst to work alongside our Financial and finance teams, building the operational and financial reporting that powers decision-making across our product lines.

The role spans both financial data (revenue, margin, projections) and operational user data (activity, engagement, behaviour across our products). One of the most valuable things this analyst will do is connect the two - testing hypotheses about how user behaviour drives Financial outcomes, and surfacing where activity patterns predict revenue, retention, or churn.

You will also be a thought partner to Financial and finance leaders - comfortable asking "what's the question behind the question" and proactive in spotting patterns we might have missed.

Responsibilities

Reporting & dashboards

• Design, build, and maintain Power BI dashboards covering financial and operational reporting across all product lines.
• Validate the accuracy of existing reports against source systems and rebuild or improve them where needed.
• Establish and document clear definitions for key metrics - financial (revenue, volume, margin) and operational (active users, engagement, retention) - so they are consistent across the business.
• Ensure dashboards refresh reliably and are trusted by the people consuming them.

Analysis & Insight

• Take questions from Financial, finance, and product leaders and turn them into structured analysis.
• Work confidently with time-series data (prices, trades, market data) to analyse trends and behaviour over time.
• Analyse operational user data - activity, engagement, feature usage, retention - to understand how users interact with our products.
• Form and test hypotheses linking user behaviour to Financial outcomes (e.g. does higher session frequency predict higher trading volume? Do users who adopt feature X retain better?).
• Proactively flag trends, anomalies, or Financial opportunities the data reveals.

Working with data

• Write SQL against our Databricks environment to extract, shape, and validate data for reporting and analysis.
• Use Python (pandas, NumPy) for ad-hoc analysis, modelling, and validation tasks that go beyond what SQL or Power BI handle comfortably.
• Join operational and Financial datasets to enable behavioural-Financial analysis, working with the Data Engineer where the join logic needs strengthening.
• Reconcile reported numbers to source systems and investigate discrepancies - both to build trust in our reporting and to flag data quality issues to the Data Engineer.
• Apply care around numerical precision and data types (e.g. integer vs float, rounding, currency handling, timezones) - small errors compound quickly in financial reporting.
• Work with the Data Engineer to request new or restructured datasets when current data shape doesn't support a question, and contribute to documentation of metric definitions.

What we're looking for:

Must-have

• Technical: Strong SQL - comfortable with joins, CTEs, window functions, and reading and debugging complex queries written by others.
• Power BI experience including building reports, semantic models, and writing DAX measures (not just consuming dashboards built by others).
• Working knowledge of Python for analysis: pandas, NumPy, ability to load data, clean it, and produce results. You won't be building pipelines, but you should be comfortable in a notebook.
• Confident in Excel for ad-hoc analysis and modelling.
• Comfortable working with both transactional/financial data and behavioural/event data - understands these are different shapes of data and need different analytical approaches.
• Comfortable working with a cloud data platform (Databricks experience preferred but not essential - Snowflake, BigQuery, Synapse equivalents fine).
• Solid understanding of data structures and types - knows the difference between int and float, understands precision issues, careful with dates and timezones.
• Basic understanding of futures markets, oil derivatives, spreads, or related trading concepts. We'll teach you the specifics, but a head start helps.
• Experience analysing large-scale time-series datasets (prices, trades, market data). Academic, thesis, or strong personal projects are acceptable evidence.
• Experience with product analytics or user behaviour tools (Mixpanel, Amplitude, Segment, Heap, GA4, or similar).
• Exposure to cohort analysis, retention curves, funnel analysis, or A/B testing methodology.
• Exposure to FP&A concepts: variance analysis, forecasting, unit economics.
• Familiarity with the Azure ecosystem (Databricks, Azure Data Lake, Power BI Service).
• Version control with Git - even at a basic level, for managing SQL, notebooks, and shared analytical work.
• Experience documenting metric definitions or contributing to a data dictionary / metrics layer.