Senior Data Engineer

Nottingham
3 weeks 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

Join our client in embarking on an ambitious data transformation journey using Databricks, guided by best practice data governance and architectural principles. To support this, we are recruiting for talented data engineers. As a major UK energy provider, our client is committed to 100% renewable energy and sustainability, focusing on delivering exceptional customer experiences.

It is initially a 6-month contract with potential to be extended. The role is Hybrid, with one day a week being based in their Nottingham office every two weeks, this is negotiable. It is a full-time role.

Accountabilities:

Develop and maintain scalable, efficient data pipelines within Databricks, continuously evolving them as requirements and technologies change.

Build and manage an enterprise data model within Databricks.

Integrate new data sources into the platform using batch and streaming processes, adhering to SLAs.

Create and maintain documentation for data pipelines and associated systems, following security and monitoring protocols.

Ensure data quality and reliability processes are effective, maintaining trust in the data.

Be comfortable with taking ownership of complex data engineering projects and develop appropriate solutions in accordance with business requirements.

Able to work closely with stakeholders and managing their requirements.

Actively coach and mentor others in the team and foster a culture of innovation and peer review within the team to ensure best practice.

Knowledge and Skills:

Extensive experience of Python preferred, including advanced concepts like decorators, protocols, functools, context managers, and comprehensions.

Strong understanding of SQL, database design, and data architecture.

Experience with Databricks and/or Spark.

Knowledgeable in data governance, data cataloguing, data quality principles, and related tools.

Skilled in data extraction, joining, and aggregation tasks, especially with big data and real-time data using Spark.

Capable of performing data cleansing operations to prepare data for analysis, including transforming data into useful formats.

Understand data storage concepts and logical data structures, such as data warehousing.

Able to write repeatable, production-quality code for data pipelines, utilizing templating and parameterization where needed.

Can make data pipeline design recommendations based on business requirements.

Experience with data migration is a plus.

Open to new ways of working and new technologies.

Self-motivated with the ability to set goals and take initiative.

Driven to troubleshoot, deconstruct problems, and build effective solutions.

Experience of Git / Version control

Experience working with larger, legacy codebases

Understanding of unit and integration testing

Understanding and experience with CI/CD and general software development best practices

A strong attention to detail and a curiosity about the data you will be working with.

A strong understanding of Linux based tooling and concepts

Knowledge and experience of Amazon Web Services is essential

Rullion celebrates and supports diversity and is committed to ensuring equal opportunities for both employees and applicants

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.

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

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.

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

Thinking about switching into data engineering in your 30s, 40s or 50s? You’re not alone. In the UK, companies of all sizes — from fintechs to government agencies, retailers to healthcare providers — are building data teams to turn vast amounts of information into insight and value. That means demand for data engineering talent remains strong, but there’s a gap between media hype and the real pathways available to mid-career professionals. This guide gives you the straight UK reality check: which data engineering roles are genuinely open to career switchers, what skills employers actually look for, how long retraining really takes and how to position your experience for success.

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

Data engineering is the backbone of modern data-driven organisations. From analytics and machine learning to business intelligence and real-time platforms, data engineers build the pipelines, platforms and infrastructure that make data usable at scale. Yet many employers struggle to attract the right data engineering candidates. Job adverts often generate high application volumes, but few applicants have the practical skills needed to build and maintain production-grade data systems. At the same time, experienced data engineers skip over adverts that feel vague, unrealistic or misaligned with real-world data engineering work. In most cases, the issue is not a shortage of talent — it is the quality and clarity of the job advert. Data engineers are pragmatic, technically rigorous and highly selective. A poorly written job ad signals immature data practices and unclear expectations. A well-written one signals strong engineering culture and serious intent. This guide explains how to write a data engineering job ad that attracts the right people, improves applicant quality and positions your organisation as a credible data employer.