Senior Backend Engineer - Data Engineer

St James's
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Data Architect Senior

Senior Data Engineer

Senior Data Scientist

Senior Data Engineer

Senior Data Engineer (AWS, Airflow, Python)

Our Energy client seeks a Senior Backend Engineer - Data Engineer to join their team in Mayfair, London.

We are looking for a Senior Backend Software Engineer with strong data engineering skills to join a small, agile team developing software solutions for our energy supply and trading functions.

Hybrid working is in play, with 3 days in the office and 2 days at home.

Senior Backend Engineer - Data Engineer - About the role:

My client’s energy business is growing rapidly with a strong focus on using advanced data systems and analytics to deliver exceptional service. We are looking for someone to take ownership of the backend architecture that underpins our analytics applications, user tools, and automated trading workflows.

You will collaborate closely with analysts, data scientists, and business stakeholders to translate requirements into robust, scalable backend solutions. You’ll be responsible for designing and developing services, APIs, data pipelines, and internal applications that integrate analytics and enable better decision-making and operational efficiency.

This is a hands-on role for someone who thrives in a fast-paced, build-first culture without multiple tiers of management. You should be excited to take full ownership of backend development, lead on best practices, and coach others in a collaborative, delivery-focused team.

Experience in retail or wholesale electricity and gas markets is helpful, but a willingness to become an expert in this field is essential. Our success is based on understanding the subject matter from first principles.

Senior Backend Engineer - Data Engineer - Key Responsibilities:

  • Architect, design, develop and maintain backend systems for analytics-driven applications, user tools, and automation workflows.

  • Build and manage APIs and internal services using Python (FastAPI, Flask) and cloud-native tooling.

  • Develop and manage data pipelines, backend components, and supporting infrastructure.

  • Manage server resources and backend processing environments to ensure reliability and scalability.

  • Monitor and maintain application performance, availability, and data quality across production systems.

  • Implement and maintain CI/CD pipelines, testing frameworks, and DevOps practices to enable robust delivery.

  • Write, test, and document code in line with quality standards and engineering best practices.

  • Collaborate with operations, analytics and commercial teams to gather requirements and translate them into scalable technical solutions.

  • Support analysts and data scientists in deploying and operationalising analytics tools and models.

  • Lead or support the data engineering team, help structure development workflows, and mentor junior team members.

  • Stay current with technological advancements and promote a culture of continuous improvement.

  • Present technical solutions to stakeholders and train non-technical users on tools and workflows.

    Senior Backend Engineer - Data Engineer - Skills Required:

  • Python (FastAPI, Flask)

  • REST API development

  • Containerisation: Docker, Kubernetes

  • CI/CD: Azure DevOps, GitHub Actions

  • Software testing and documentation practices

  • SQL, PySpark, Databricks

  • Relational databases and data lake architecture

  • Model and data pipeline integration (e.g. MLflow)

  • Streamlit or other lightweight UI frameworks

  • Microsoft Azure (Functions, Storage, Compute)

  • Monitoring tools (Grafana, Prometheus, etc.)

  • Performance optimisation and resource management

  • Agile delivery practices (Jira, Azure Boards, etc.)

  • Strong communication with technical and business teams

  • Mentoring and knowledge sharing within the team

    Desirable Skills:

  • Experience in energy supply or trading

  • Familiarity with dbt or modular analytics tooling

  • Exposure to forecasting or optimisation workflows

  • Knowledge of React or frontend tools for internal apps

  • Networking or IoT integration experience

    What they offer:

  • A high-autonomy role in a flat, delivery-focused team

  • Ownership of backend systems for real-time analytics and automation

  • A fast-moving, hands-on culture with meaningful technical challenges

  • The opportunity to apply software and data engineering to real-world energy problems

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.