Senior Data Engineer

RES
Glasgow
3 weeks ago
Create job alert
Description

Do you want to work to make Power for Good?


We're the world's largest independent renewable energy company. We're driven by a simple yet powerful vision: to create a future where everyone has access to affordable, zero carbon energy.


We know that achieving our ambitions would be impossible without our people. Because we're tackling some of the world's toughest problems, we need the very best people to help us. They're our most important asset so that's why we continually invest in them.


RES is a family with a diverse workforce, and we are dedicated to the personal professional growth of our people, no matter what stage of their career they're at. We can promise you rewarding work which makes a real impact, the chance to learn from inspiring colleagues from across a growing, global network and opportunities to grow personally and professionally.


Our competitive package offers rewards and benefits including pension schemes, flexible working, and top-down emphasis on better work-life balance. We also offer private healthcare, discounted green travel, 25 days holiday with options to buy/sell days, enhanced family leave and four volunteering days per year so you can make a difference somewhere else.


The Position

We are looking for a Senior Data Engineer with advanced expertise in Databricks to lead the development of scalable data solutions across in our asset performance management software, within our Digital Solutions business.


This role involves architecting complex data pipelines, mentoring junior engineers, and driving best practices in data engineering and cloud analytics. You will play a key role in shaping our data strategy which is the backbone of our software and enabling high-impact analytics and machine learning initiatives.


Accountabilities

  • Design and implement scalable, high-performance data pipelines.
  • Work with the lead cloud architect on the design of data lakehouse solutions leveraging Delta Lake and Unity Catalog.
  • Collaborate with cross-functional teams to define data requirements, governance standards, and integration strategies.
  • Champion data quality, lineage, and observability through automated testing, monitoring, and documentation.
  • Mentoring and guidance of junior data engineers. Using your passion for data engineering to foster a culture of technical excellence and continuous learning.
  • Driving the adoption of CI/CD and DevOps practices for data engineering workflows.
  • Stay ahead of emerging technologies and Databricks platform updates, evaluating their relevance and impact.

Knowledge

  • Deep understanding of distributed data processing, data lakehouse architecture, and cloud-native data platforms.
  • Optimization of data workflows for performance, reliability, and cost-efficiency on cloud platforms (particularly Azure but experience with AWS and/or GCP would be beneficial).
  • Strong knowledge of data modelling, warehousing, and governance principles.
  • Knowledge of data privacy and compliance standards (e.g., GDPR, HIPAA).
  • Understanding of OLTP and OLAP and what scenarios to deploy them in.
  • Understanding of incremental processing patterns.

Skills

  • Strong proficiency in Python and SQL. Experience working with Scala would be beneficial.
  • Proven ability to design and optimize large-scale ETL/ELT pipelines.
  • Building and managing orchestrations.
  • Excellent oral and written communication, both within the team and with our stakeholders.

Experience

  • 5+ years of experience in data engineering, with at least 2 years working extensively with Databricks and orchestrated pipelines such as DBT, DLT, or workflows using jobs.
  • Experience with Delta Lake and Unity Catalog in production environments.
  • Experience with CI/CD tools and version control systems (e.g., Git, GitHub Actions, Azure DevOps, Databricks Asset Bundles).
  • Experience with real-time data processing, both batch and streaming.
  • Experience working on machine learning workflows and integration with data pipelines.
  • Experience leading data engineering projects with distributed teams, ideally in a cross functional environment.

Qualifications

  • Databricks Certified Data Engineer Professional or equivalent certification.

At RES we celebrate difference as we know it makes our company a great place to work. Encouraging applicants with different backgrounds, ideas and points of view, we create teams who work together to solve complex problems and design practical solutions for our clients. Our multiple perspectives come from many sources including the diverse ethnicity, culture, gender, nationality, age, sex, sexual orientation, gender identity and expression, disability, marital status, parental status, education, social background and life experience of our people.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

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 Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.