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- Tasks: Dive deep into messy energy market data and transform it into usable insights using Python.
- Company: Join a forward-thinking company focused on data-driven solutions in the energy sector.
- Benefits: Enjoy remote work, competitive pay, and opportunities for professional growth.
- Other info: Flexible remote-first environment with a focus on collaboration and learning.
- Why this job: Make a real impact by solving complex data challenges and enhancing data quality.
- Qualifications: Strong Python and SQL skills, with experience in data cleaning and reconciliation.
The predicted salary is between 50000 - 65000 £ per year.
We need someone who understands data deeply and uses Python to wrangle it — not a platform engineer, not a pure pipeline builder, but a data specialist who is comfortable with research, investigation, and the unglamorous work of making messy energy market data actually usable. You’ll spend significant time on tasks like mapping BM units to power plants and fuel types, reconciling legacy data formats with current ones, ensuring consistency between different Elexon message types, and cleaning time-series data (outliers, gaps, overlaps). Some of this requires genuine investigation — cross-referencing sources, making judgment calls, documenting edge cases. There’s no API that solves these problems for you.
Python is your primary tool (Pandas, NumPy, standard libraries) to minimise manual effort, but you should be comfortable that some detective work is unavoidable. If you find satisfaction in truly understanding a dataset’s structure and quirks — rather than just piping data through and hoping for the best — this role is for you.
Data Mapping and Research- Map BM units from Elexon to their corresponding power plants, substations, and fuel types — combining API data, public registers, and manual research
- Map substations to ETYS zones and grid supply points
- Build and maintain reference/master datasets that link identifiers across disparate sources (Elexon, National Grid ESO, TEC register, etc.)
- Document mappings, assumptions, and known limitations clearly for downstream users
- Reconcile legacy data formats with current formats (e.g., historical operational data stored in different schemas or granularities)
- Ensure consistency between different Elexon message types — understand the market data structure well enough to know why BOALF, BOD, and DISBSAD might not perfectly align and how to handle it
- Investigate discrepancies between data sources and determine authoritative values
- Clean time-series data: detect outliers (price spikes, meter errors), fill gaps appropriately, resolve overlapping or duplicate timestamps
- Develop reusable Python-based cleaning routines that can be applied across datasets
- Understand why data quality issues occur (settlement reruns, late submissions, format changes) not just patch them
- Write and maintain Python data grabbers for energy market APIs
- Build dbt models to transform raw data into clean, analysis-ready datasets
- Orchestrate workflows via GitHub Actions
- Design PostgreSQL schemas that reflect your understanding of the domain
- Must Have
- Strong Python skills for data work — you’re fluent with pandas, comfortable writing clean, testable code, and can build reusable data processing logic. This is not an Excel role
- Solid SQL skills — complex queries, window functions, CTEs in PostgreSQL
- Experience with messy, real-world data — you’ve done reconciliation, cleaning, or mapping work before and understand it’s not always automatable
- Methodical and detail-oriented — you notice inconsistencies and want to understand root causes
- Good documentation habits — you know that undocumented mappings and assumptions are technical debt
- Self-directed — you can own ambiguous problems, do your own research, and communicate findings clearly
- Nice to Have
- Experience with energy, utilities, or market data (any geography)
- Familiarity with UK energy markets, Elexon data, or grid operations
- dbt experience for transformation pipelines
- Exposure to time-series data challenges (irregular timestamps, gaps, restatements)
- Highly Desirable — Agentic AI Coding Experience
- We value candidates who can build software using agentic AI coding systems. This is fundamentally different from using code completion tools or chat-based assistants.
- GitHub Copilot (code completion/autocomplete)
- ChatGPT or similar chat interfaces for generating isolated code snippets
- Any tool that only provides single-turn question/answer interactions
- Hands-on experience with agentic coding systems such as Claude Code, Codex (OpenAI's agentic coding tool), Open Code, or Cursor.
- Ideal candidates will demonstrate:
- Breadth of experience — proficiency with at least 2 agentic systems (experience with only one is insufficient)
- End-to-end development — ability to design and build software from the ground up using these tools, not just generating isolated snippets
- Multi-agent orchestration — demonstrated experience orchestrating multiple agents using skills, tools, and agent coordination, not just one-shot problem solving
- Deep system knowledge — familiarity with hooks, permission systems, MCP (Model Context Protocol) servers, custom skills and tool definitions, and context management
- Platform/infrastructure engineers who prefer to stay above the data layer
- People who expect clean, well-documented data as input
- Those uncomfortable with research, ambiguity, or 'manual' investigation work
- Remote-first with async collaboration (Slack, GitHub, documented decisions)
- Core overlap with UK business hours expected (at least 4 hours daily)
- Competitive compensation based on location and experience
- Plenty of opportunities for learning and professional growth
- B2b contract with a paid vacation
Senior Data Specialist in London employer: Alex Staff
Contact Detail:
Alex Staff Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Specialist in London
✨Tip Number 1
Get your networking game on! Reach out to folks in the energy sector or data specialists on LinkedIn. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your Python skills! If you've got a GitHub profile, make sure it’s up to date with projects that highlight your data wrangling prowess. Potential employers love seeing real examples of your work.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to discuss how you’ve tackled messy data before — think about specific challenges and how you overcame them.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love candidates who take that extra step!
We think you need these skills to ace Senior Data Specialist in London
Some tips for your application 🫡
Show Off Your Python Skills: Make sure to highlight your Python expertise in your application. We want to see how you've used libraries like Pandas and NumPy to tackle messy data. Share specific examples of projects where you’ve wrangled data and cleaned it up!
Be Detail-Oriented: We love candidates who pay attention to the nitty-gritty details. In your application, mention any experience you have with data reconciliation or cleaning. Talk about how you’ve tackled inconsistencies and what methods you used to ensure data quality.
Document Your Process: Good documentation habits are key for us. When applying, include examples of how you've documented your work in the past. This could be anything from mapping assumptions to detailing your data cleaning processes. It shows you understand the importance of clarity for downstream users!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to showcase your enthusiasm for joining our team!
How to prepare for a job interview at Alex Staff
✨Know Your Data Inside Out
Before the interview, dive deep into the types of messy data you might encounter in this role. Familiarise yourself with energy market data, Elexon messages, and common inconsistencies. Being able to discuss specific examples of how you've tackled similar data challenges will show your understanding and passion for the work.
✨Show Off Your Python Skills
Prepare to demonstrate your Python prowess, especially with libraries like Pandas and NumPy. Think of a couple of projects where you've built reusable data processing logic or cleaning routines. Be ready to explain your thought process and the impact of your code on data quality.
✨Be Ready for Detective Work
This role requires a bit of sleuthing! Brush up on your investigative skills and be prepared to discuss how you've approached ambiguous problems in the past. Highlight your methods for cross-referencing sources and documenting findings, as this will resonate well with the interviewers.
✨Communicate Clearly and Document Well
Good documentation habits are crucial. During the interview, emphasise your approach to documenting mappings, assumptions, and limitations. Share examples of how clear communication has helped your team in previous roles, especially when dealing with complex datasets.