Senior Data Engineer

South Bank
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer (Databricks)
Location: London (Hybrid)
Rate: Negotiable, depending on experience
Duration: 6 months (initial)

We’re looking for a Senior Data Engineer (Databricks) to join a world-leading energy organisation on a key transformation programme within their trading and supply division. This is an exciting opportunity to play a pivotal role in building modern, scalable data solutions using Azure cloud technologies.

The Role

As a Senior Data Engineer, you’ll be responsible for designing and developing robust data foundations and end-to-end solutions that drive value across the business. You’ll help shape and embed data-driven thinking across both technical and business teams, ensuring the organisation continues to lead with insight and innovation.

You’ll act as a subject matter expert, guiding technical decisions, mentoring junior engineers, and ensuring data engineering best practices are consistently applied.

Key Responsibilities
Design and build data solutions aligned with business and IT strategy.
Lead development of scalable data pipelines and models using Azure and Databricks.
Support data foundation initiatives and ensure effective rollout across business units.
Act as a bridge between technical and non-technical stakeholders, presenting insights clearly.
Oversee change management, incident management, and data quality improvement.
Contribute to best practice sharing and community-building initiatives within the data engineering space.Required Skills & Experience
Cloud Platforms: Strong expertise in AWS / Azure / SAP
ETL/ELT Pipelines: Advanced proficiency
Data Modelling: Expert level
Data Integration & Ingestion: Skilled
Databricks, SQL, Synapse, Data Factory and related Azure services
Version Control / DevOps tools: GITHUB, Azure DevOps, Actions
Testing & Automation tools: PyTest, SonarQubeDesirable Experience
Experience leading or running scrum teams
Exposure to planning tools such as BPC
Familiarity with external data ecosystems and documentation tools (e.g., MKDocs)The Project

You’ll be joining a large-scale programme focused on modernising a global data warehouse platform using Azure technologies. The project aims to deliver a unified and standardised view of data across international operations — a key enabler for smarter, data-driven trading decisions.

If you’re a data engineer with deep Azure and Databricks experience, and you enjoy solving complex challenges within a global business, this contract offers a chance to make a real impact on a high-profile initiative.

Interested? 

Please apply now with your updated CV and reach out to Tom Johnson at Certain Advantage - Ref: 79413

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.

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

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.