Junior Data Specialist - UK Energy Markets & Grid Analytics

Highview Power is a small but growing global organisation who are leading the way towards a cleaner, more efficient and secure energy future.

Our proprietary long duration, zero emissions energy storage system utilises cryogenic technology and surplus electricity; at times of low demand/low cost, to make liquid air which can be stored and later converted back into energy and released into the grid, at times of high demand/high cost.

This award-winning technology has been dubbed as "the missing link" to making Renewable Green Energy sources a more resilient, reliable and cost-effective option when compared with traditional fossil fuel alternatives.

Highview Power values it’s employees and are committed to creating a positive, inspiring and inclusive working environment.

Job summary / Purpose of the role

We're looking for a curious, methodical data specialist who wants to deeply understand messy real-world energy data, and use Python to wrangle it into shape.

This is not a role where you build pipelines and walk away. You'll spend significant time investigating, mapping, and cleaning data from UK energy markets: figuring out which BM units belong to which power plants, mapping substations to ETYS zones, reconciling legacy data formats with current ones, and understanding why two Elexon message types don't quite agree with each other.

Some of this work requires genuine research and investigation — there's no API that tells you the fuel type of every BM unit or which substation feeds which grid supply point. You'll need to cross-reference sources, make judgment calls, and document your findings. Python and its data libraries (pandas, etc.) are your main tools to minimise manual work, but you should be comfortable with the reality that some detective work is unavoidable.

Typical Tasks You'll Work On

• Data mapping and research: Map BM units from Elexon to their corresponding power plants, substations, and fuel types — combining API data with manual research
• Map substations to ETYS zones and grid supply points
• Build and maintain reference datasets that link identifiers across different data sources
• Document mappings, assumptions, and edge cases clearly

Data reconciliation and cleaning:

• Reconcile legacy data formats with current formats (e.g., historical meter data stored differently than recent data)
• Ensure consistency between different Elexon message types (understanding why BOALF and BOD might not perfectly align)
• Clean time-series data: detect and handle outliers, fill gaps, resolve overlapping timestamps Investigate and resolve data quality issues — understanding why something looks wrong, not just flagging it

Pipeline development (supporting the above):

• Write Python scripts to automate data ingestion from energy market APIs (Elexon, National Grid ESO)
• Build transformation logic using SQL and dbt to structure data for analysis
• Use GitHub Actions to schedule and orchestrate data workflows

Requirements

• Recent graduate (or final year) in a quantitative field — Computer Science, Physics, Mathematics, Engineering, Economics, or similar
• Solid Python programming skills — you can write clean, readable code using pandas, numpy, and standard libraries. This is not an Excel role.
• Comfortable with SQL for querying and transforming data
• Methodical and detail-oriented — you notice when data doesn't look right and want to understand why Good at documenting your work and explaining your reasoning
• Patient with ambiguity — comfortable when there's no perfect answer and you need to make reasoned decisions

Nice to have:

• Experience cleaning or reconciling messy datasets (coursework, projects, internships)
• Exposure to Git, dbt, or any data transformation tooling
• Interest in energy markets, power systems, or sustainability
  
  What we're NOT looking for:
• Someone who wants to focus on infrastructure, cloud platforms, or DevOps
• Someone who prefers clean, well-documented data handed to them
• Someone uncomfortable with research and investigation tasks
  
  Why Join Us?
• Learn a fascinating domain: UK energy markets are complex and increasingly critical for the energy transition
• Develop rare expertise: Understanding the structure of GB balancing market data is genuinely valuable and hard to find
• Real impact: Your data work directly supports trading decisions and grid analytics
• Modern tooling: Python, dbt, PostgreSQL, GitHub Actions — no legacy Excel workflows
  

Benefits

• Private Medical and Dental Insurance
• Financial Wellbeing Support Platform, including hunting down lost pensions, access to Independent Financial Advisors, and retail discounts.
• Attractive salary package
• Annual salary review at management's discretion
• 25 days of paid annual leave
• Automatic enrolment in the pension scheme after 3 months of service, with the option for salary sacrifice
• Season ticket loan available
• Opportunities for Learning and Development