Renewable energy jobs · Data

Data Jobs in Renewable Energy

Data professionals in renewable energy build and maintain the analytical infrastructure that turns raw operational readings - from turbine sensors, solar inverters, grid meters, and weather stations - into decisions about maintenance schedules, energy trading, and grid balancing. The sector generated over 16.6 million jobs globally in 2024, according to IRENA's annual review, and the data function is growing faster than most other roles as digitalisation accelerates across the industry.

What data roles actually involve in energy

The work differs from data roles in tech or finance in one important respect: renewable energy data is physical. Sensor arrays on a single offshore wind farm can produce terabytes of vibration, temperature, and power output data daily. Data engineers design the pipelines that ingest, clean, and store this information; data analysts build the dashboards that operations teams rely on for real-time monitoring; data scientists develop the predictive models that forecast equipment failures before they happen.

The IEA estimates that AI-based fault detection can reduce grid outage durations by 30-50%, and that up to 175 GW of transmission capacity could be unlocked through AI-driven grid management alone - without building a single new line. Behind each of these gains sits a data team that built the models, validated the inputs, and maintains the infrastructure in production.

Who is hiring

The employers advertising the most data positions on Rejobs span utilities, pure-play renewables, and energy tech. Enexis, the Dutch grid operator, leads with 23 data-tagged roles in the past year, followed by UK-based OVO Energy and Octopus Energy. On the developer side, EDP Renewables and SSE Renewables regularly seek data engineers and scientists. Grid technology specialist Landis+Gyr and energy software company Uplight round out the top hirers, reflecting how metering, demand response, and customer analytics drive data hiring alongside generation and grid operations.

In-demand roles and skills

Data Engineer is the single most common job title in this space, followed by Data Analyst and Data Scientist. Senior and lead variants appear frequently, signalling that employers need experienced professionals who can architect systems, not just run queries. The tech stacks typically involve Python, SQL, cloud platforms (AWS and Azure dominate), and tools like Apache Spark, Airflow, and dbt for pipeline orchestration. Domain-specific knowledge of SCADA systems, time-series databases, and energy market structures gives candidates a clear edge.

Energy analytics roles - where data skills meet domain expertise in generation forecasting, grid balancing, or energy trading - tend to command a salary premium over general data positions. Professionals who combine machine learning competence with an understanding of power systems are particularly scarce.

Where the jobs are concentrated

London is the largest hub, with 80 data-tagged positions, driven by trading desks, utility headquarters, and the UK's competitive energy tech scene. Berlin, Hamburg, and Munich collectively account for nearly 150 roles, reflecting Germany's Energiewende and the density of grid operators and solar developers there. Glasgow, Bristol, Madrid, Amsterdam, and Copenhagen round out the top locations across Europe.

The investment context

Europe's grid digitalisation is backed by serious capital. The European Commission expects EUR 170 billion in grid digitalisation investment by 2030, covering smart meters, automated grid management, and field operations technology. Investment in analytics for grid operations and asset performance has already more than doubled since 2016, reaching $1.48 billion in 2022. Every euro of that spending creates demand for people who can make sense of the data these systems produce.

For data professionals considering the sector, the combination of regulatory momentum, infrastructure investment, and the physical complexity of managing distributed energy resources makes renewable energy one of the most interesting data domains to work in - and one where the problems are far from solved.