Leicester
FinOps Data Analyst Up to £47,000 | Leicester | Hybrid (4 days onsite) About the Role We're working with a major UK retail brand to hire a FinOps Data Analyst for their Finance Analytics team. You'll provide analytical support and reporting solutions across multiple finance functions, working closely with SQL engineers and Finance stakeholders. This hands-on role uses SQL and Python daily to explore data, identify trends, and deliver actionable insights that drive financial decision-making. The team is modernising their data platform with Databricks and Medallion Architecture, giving you exposure to cutting-edge technologies. Key Responsibilities • Build analytical solutions and reporting across 4 finance areas: Accounts Payable, Cash Accounting, Commercial Services, and Operations., • Perform SQL-based data exploration, validation, and transformation., • Use Python (Pandas/Numpy) for analysis, automation, and data profiling., • Build Power BI dashboards to visualise financial metrics., • Support ad-hoc analysis by exploring trends and anomalies., • Engage with stakeholders to gather requirements and deliver analytical outputs., • Databricks Modernisation: Exposure to Databricks as the team builds Gold Standard Medallion Architecture., • Self-Service Analytics: Reducing ad-hoc queries (currently 60-70% of workload) by building reusable assets., • BAU Finance Support: Ongoing analytics across AP, Cash Accounting, Commercial Services, and Operations., • Analytical Automation: Using Python/SQL to streamline recurring finance analysis. Essential: • Strong SQL (querying, joins, CTEs, window functions, data profiling)., • Python for data analysis (Pandas, Numpy)., • Power BI experience (dashboard creation, no heavy DAX required)., • Strong analytical mindset and communication skills., • Databricks or modern cloud data platforms., • Experience within a Finance team or working with financial data., • Salary up to £47,000., • Exposure to modern data tech (Databricks, Medallion Architecture)., • ML/AI exposure as the team evolves., • Hybrid working (4 days onsite after initial training)., • Stage 1: Informal discussion with Analytics Manager (45 mins, virtual)., • Stage 2: In-person assessment (3 hours total)., • 2 hours: Analytical task using SQL/Python on a provided dataset., • First 3 months: 5 days/week onsite for training.