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Data Analyst

Job in Wokingham, Berkshire, RG40, England, UK
Listing for: Test Triangle
Full Time position
Listed on 2026-06-20
Job specializations:
  • IT/Tech
    Data Analyst, Data Scientist
Salary/Wage Range or Industry Benchmark: 40000 - 60000 GBP Yearly GBP 40000.00 60000.00 YEAR
Job Description & How to Apply Below

Role Purpose

Support Energy Insights & Analysis (EIA) and Future Energy Scenarios (FES) by delivering high‑quality analysis, dashboards and data products on NESO’s Data & Analytics Platform (DAP) using Palantir Foundry. The role focuses on transforming complex energy system data into trusted insights that inform modelling, scenario development and strategic decision‑making.

Key Responsibilities
  • Perform exploratory and structured analysis on large, complex datasets (time‑series, scenario, market, network and policy data)
  • Support EIA/FES modelling teams by preparing, validating and analysing input and output datasets
  • Develop reproducible analysis workflows using SQL and Python within Foundry
  • Produce clear, well‑structured insight packs, visualisations and narratives for senior and non‑technical stakeholders
  • Work closely with Data Scientists, Data Engineers and Modellers to align analysis with business and policy questions
  • Ensure data outputs meet quality, governance, lineage and assurance requirements
  • Contribute to continuous improvement of analytical standards, templates and reusable assets across EIA/FES
  • Strong experience in SQL and data analysis using Python
  • Experience analysing time‑series and multi‑source datasets
  • Strong data visualisation and storytelling skills
  • Ability to explain analytical findings clearly to non‑technical audiences
  • Experience in energy systems, utilities, infrastructure or public‑sector analytics
  • Familiarity with scenario analysis, modelling outputs or forecasting concepts
  • Experience working in assurance‑led or regulated environments
  • Understanding of DAP and Foundry concepts (data products, lineage, governed analytics)
Ways of Working
  • Strong partnership with modelling, policy and digital teams
  • Focus on accuracy, transparency and reusability
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