Lead Data Engineer

Octopus Energy
London
11 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer (Azure)

Lead Data Engineer / Architect – Databricks Active - SC Cleared

Octopus Energy supplies electricity and gas to over 7 million UK householders and is at the forefront of innovation in the drive towards net zero. Our Gross Margin team produces key financial reporting and analysis to support decision making and margin improvements across the UK business. We also support teams in other countries where Octopus operates.The Gross Margin pipeline - for which you will be responsible along with a team of three engineers - uses a vast amount of data and produces a highly granular view of consumption, revenue and costs. Challenges include the quantity and quality of data and the estimations required where data does not exist. We have a superb pipeline with a very exciting dataset, analysis from which provides valuable information s across the business, driving development of Octopus’ industry-leading Time Of Use tariffs, refining financial forecasting, and preventing multi-million pound write-offs. The Lead Data Engineer will drive the technical vision behind this pipeline as our customer base continues to grow and market development towards net zero requires an increasingly data-heavy calculation of energy consumption, and will also take joint responsibility for the accuracy of our outputs and guiding engineers in the team on their own career progression.

What You'll Do...

Product Leadership:You will shape the design of the Gross Margin pipeline so it continues to accurately reflect actual revenue and costs when reconciled against internal billing systems and external industry flow data, whilst also making it more accessible and valuable to teams across the wider Octopus business.People Leadership:You will support and develop the technical progression of top-tier engineering talent while fostering an inclusive and productive team environment.Technical Leadership:You will oversee the technical and delivery outcomes of our Gross Margin pipeline for maximum scalability, efficiency and simplicity whilst driving best practices within the codebase. 

What You'll Need...

Proven Track Record: History of shipping product updates, getting things done, and leading teams in high-growth, product-focused organisations. Management and Leadership Experience: 2+ years managing high-performance engineering teams, with a focus on people leadership, providing support, coaching, and development. Modern Data Expertise: 5+ years building highly scalable technology products using SQL and Python. Experience with Databricks, dbt, Spark and AWS would be a big plus. Commercial Mindset: Experience in financial reporting is not required (although a plus), but appreciation for the business implications of what the data shows us is key. Test Driven Development: Passion for ensuring code changes improve our view of Gross Margin and don’t inadvertently break anything else. Hands-on Approach: You are eager to contribute to design and code alongside your team. Engineering Excellence: Fostering a high bar for engineering excellence, holding teams accountable for technical outcomes and individual growth. Balanced Decision-Making: Ability to make trade-offs between product velocity and technical debt to iteratively improve the pipeline. Technical Influence: Partnering with other engineers to drive initiatives that elevate best practices. Stakeholder Relationships: Building strong relationships with other teams to ensure they are able (and know how) to get the most benefit from the pipeline.

Our Data Stack...

SQL and Python based pipelines built with dbt on Databricks Analysis via Python jupyter notebooks Pyspark in Databricks workflows for heavy lifting Streamlit and Python for dashboarding Airflow DAGs with Python for ETL Notion for data documentation

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.