Interim Data Analyst – Traded Services Birmingham (Hybrid – 1 day per month in office)
£400–£450 per day
Initial 3-month interim assignment
We are supporting a major local authority in recruiting an experienced Interim Data Analyst to contribute to a high‑profile Traded Services Review. This is a fantastic opportunity to deliver impactful analytical insight that directly informs service redesign,mercial improvements, and financial sustainability. As an Interim Data Analyst, you will take the lead on analysing, interpreting, and presentingplex operational and financial data to support strategic decision‑making. Working closely with service leads, finance colleagues, and project stakeholders, you will develop robust datasets, build clear analytical models, and produce high‑quality dashboards and reports that shape the future model for traded services.
Key Responsibilities
1. Acquire, cleanse, validate, and prepare data across traded service areas.
2. Build structured datasets and documentation aligned withernance and data‑quality standards.
3. Analyse trends, cost drivers, ie patterns, and service performance to generate actionable insight.
4. Develop financial and operational models to support pricing, efficiency, and viability assessments.
5. Produce clear dashboards (Power BI, Excel) and reports for senior audiences.
6. Collaborate closely with service managers, finance partners, and project teams.
7. Contribute to continuous improvement by rmending enhancements to data processes and performance frameworks.
Essential Skills & Experience
8. Strong experience in traded services.
Strong analytical and problem‑solving skills.
9. Proficiency in SQL for data extraction and transformation.
10. Working knowledge of Python for analysis and automation.
11. Advanced Excel capabilities (including Power Query and modelling).
12. Experience creating Power BI dashboards and analytical outputs.
13. Ability to translateplex data into clear, accessible insights for non‑technical stakeholders.
14. Experience working with large orplex datasets in a fast‑moving environment.