Data Specialist

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• We are looking for an individual with a profound understanding of data who utilizes Python for data manipulation — not a platform engineer or a mere pipeline builder, but a data expert who is at ease with research, exploration, and the often unglamorous task of transforming messy energy market data into a usable format.

• Your role will involve significant engagement with tasks such as: mapping BM units to power plants and fuel types, reconciling legacy data formats with current ones, ensuring consistency across various Elexon message types, and cleaning time-series data (including outliers, gaps, and overlaps). Some of these tasks will necessitate genuine investigative work — cross-referencing sources, making informed decisions, and documenting edge cases. There is no API that addresses these challenges for you.

• Python will be your primary tool (utilizing Pandas, Numpy, and standard libraries) to reduce manual effort, yet you should be aware that some detective work is inevitable. If you find fulfillment in truly comprehending a dataset's structure and intricacies — rather than merely passing data through and hoping for optimal results — this position is tailor-made for you.

• Data Mapping and Research

• Map BM units from Elexon to their respective power plants, substations, and fuel types — integrating API data, public registers, and manual research.

• Associate substations with ETYS zones and grid supply points.

• Develop and maintain reference/master datasets that connect identifiers across various sources (Elexon, National Grid ESO, TEC register, etc.).

• Clearly document mappings, assumptions, and known limitations for downstream users.

• Data Reconciliation and Consistency

• Reconcile legacy data formats with current formats (e.g., historical operational data stored in different schemas or granularities).

• Ensure consistency among different Elexon message types — comprehend the market data structure sufficiently to recognize why BOALF, BOD, and DISBSAD may not align perfectly and how to manage those discrepancies.

• Investigate inconsistencies between data sources and ascertain authoritative values.

• Data Cleaning and Quality

• Clean time-series data: identify outliers (such as price spikes or meter errors), appropriately fill gaps, and resolve overlapping or duplicate timestamps.

• Create reusable Python-based cleaning routines applicable across datasets.

• Understand the underlying reasons for data quality issues (settlement reruns, late submissions, format changes) rather than just implementing fixes.

• Pipeline Development (Supporting the Above)

• Write and maintain Python data grabbers for energy market APIs.

• Develop dbt models to convert raw data into clean, analysis-ready datasets.

• Coordinate workflows using GitHub Actions.

• Design PostgreSQL schemas that embody your domain knowledge.

⛳️ Requirements

• Strong Python skills for data work — you are proficient with pandas, capable of writing clean, testable code, and can create reusable data processing logic. This position is not Excel-centric.

• Solid SQL skills — adept at complex queries, window functions, and CTEs in PostgreSQL.

• Experience with messy, real-world data — you have previously engaged in reconciliation, cleaning, or mapping work and understand that it is not always automatable.

• Methodical and detail-oriented — you are attuned to inconsistencies and seek to understand their root causes.

• Good documentation habits — you recognize that undocumented mappings and assumptions create technical debt.

• Self-directed — you can take ownership of ambiguous problems, conduct your own research, and communicate findings effectively.

• Nice to Have

• Experience with energy, utilities, or market data (from any geography).

• Familiarity with UK energy markets, Elexon data, or grid operations.

• dbt experience for transformation pipelines.

• Exposure to challenges associated with time-series data (such as irregular timestamps, gaps, or restatements).

• Highly Desirable — Agentic AI Coding Experience

• We appreciate candidates capable of developing software using agentic AI coding systems. This skill set is fundamentally distinct from merely utilizing code completion tools or chat-based assistants.

• Not What We're Looking For

• Platform/infrastructure engineers who prefer to operate above the data layer.

• Individuals who expect clean, well-documented data as input.

• Those who are uncomfortable with research, ambiguity, or manual investigative tasks.

🏝️ Benefits

• Numerous opportunities for learning and professional development.

• B2B contract with paid vacation.