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Financial Data Analyst – Private Equity (Data & Analytics Team)
London - 4 days in-office
Up to £75,000
Join a global private equity leader across secondary markets. A niche but fast-growing space with complex data challenges and huge strategic importance.
The Company
This firm is investing heavily in its data and analytics capabilities, with a newly established team spearheading the build of scalable tools and platforms to support better, faster investment decisions. If you're passionate about creating order from chaos, building impactful tools, and making data matter, this role offers a rare opportunity to shape a data function from the ground up.
Key Responsibilities
* Build and maintain Power BI dashboards to track portfolio performance, risk, and cash flows, translating raw data into clear insights.
* Partner with Data Engineering to validate pipelines, ensure data quality, and automate reporting processes.
* Deliver interactive reports that support fund analytics, portfolio oversight, and executive decision-making.
* Handle ad hoc data requests and investigate anomalies, highlighting key trends and issues.
* Collaborate with a Data Steward to define and enforce data management and reconciliation standards.
* Document workflows and promote best practices for consistent, scalable analytics.
* Prepare clear, data-driven presentations for senior stakeholders and investment committees.
Expertise & Qualifications
* Strong experience building dashboards and reports with Power BI, Qlik, or similar tools
* Strong Excel skills, including pivot tables and Power Query
* Solid understanding of data quality practices such as validation, exception reporting, and automation
* Clear communicator with the ability to work across technical and non-technical teams
* Exposure to financial services or private equity is a plus
* Familiarity with Python, Snowflake, or dbt is beneficial but not required
Why Join?
* Greenfield opportunity, help build the firm’s internal BI platform from scratch
* High-impact, visible role in a firm committed to data-driven transformation
* A genuinely collaborative team with a bias for action and iteration, not perfection
* Strong support from leadership and investment teams who want better, faster, cleaner data
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