Senior Data Engineer

Motorsport Network
Wantage
1 week ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Energy

Senior Data Engineer, SQL, RDBMS, AWS, Python, Mainly Remote

Senior Data Engineer (2 days onsite in London)

We are looking for a Senior Data Engineer to join our Technology & Innovation Group. As a Senior Data Engineer, you will play a key role in shaping the future of Atlassian Williams Racing’s data platform. You will be responsible for designing, implementing, and evolving scalable, secure, and high-performance data infrastructure and pipelines, while also contributing to the strategic direction of our data architecture.

This role bridges deep technical expertise with business context, ensuring the data engineering layer effectively supports performance, operations, and innovation across the organisation. As we scale up our data platform, you will also mentor junior engineers, influence architectural decisions, and help integrate new technologies that support Williams' broader transformation journey.

Main duties:

  • Lead the design, development, and optimisation of modern, cloud-native data pipelines and infrastructure to support large-scale, high-value data workloads across the organisation.

  • Work closely with Data Architects and the Head of Data & AI to support the development and implementation of our Data Strategy and the build-out of our data platform.

  • Evaluate and implement best-in-class technologies, frameworks, and tools for ingestion, processing, governance, observability, and storage of structured and unstructured data.

  • Collaborate across business and technical teams to identify requirements, develop solutions, and ensure that data products support analytics, AI/ML, and operational reporting use cases.

  • Champion data quality, observability, lineage, and metadata management to ensure data is trusted, discoverable, and reliable.

  • Drive cloud migration efforts, including deployment of scalable services in AWS (or other cloud environments), infrastructure as code, and automation of data operations.

  • Provide guidance and mentorship to other engineers, helping grow a culture of high-performance engineering and continuous improvement.

  • Create and maintain robust documentation of pipelines, architecture, and best practices to ensure sustainability and knowledge sharing.

Skills and experience required:

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

  • Previous, proven,hands-on experience in data engineering or software engineering with strong exposure to modern data platforms.

  • Proven expertise in building and maintaining data pipelines and ETL/ELT workflows using tools like Apache Airflow, dbt, or custom frameworks.

  • Strong experience with cloud data platforms (e.g., AWS, Azure, GCP) and distributed data systems (Spark, Kafka, or Flink, etc)

  • Proficiency in Python (or similar languages) with solid software engineering fundamentals (testing, modularity, version control).

  • Hands-on experience with SQL and NoSQL data stores, such as PostgreSQL, Redshift, DynamoDB, or MongoDB.

  • Good understanding of data warehousing and modern architectures (e.g., data lakehouse, data mesh).

  • Familiarity with DevOps/CI-CD practices, infrastructure-as-code (Terraform, CloudFormation), and containerisation (Docker/Kubernetes).

  • Understanding of data quality, observability, lineage, and metadata management practices.

Desirable:

  • Experience with event-driven architectures and real-time data processing.

  • Prior exposure to data governance, cataloguing, and security frameworks (e.g., IAM, encryption, GDPR).

  • Experience in a fast-paced environment such as automotive, motorsport, or high-performance computing.

  • A track record of mentoring junior engineers and contributing to engineering culture and team standards.

Company Description

For almost 50 years, Williams Racing has been at the forefront of one of the fastest sports on the planet, being one of the top three most successful teams in history competing in the FIA Formula 1 World Championship. With an almost unrivalled heritage of engineering and racing F1 cars and unforgettable eras that demonstrate it is a force to be reckoned with, the British squad boasts 16 F1 World Championship titles to its name.

Since its foundation in 1977 by the eminent, late Sir Frank Williams and engineering pioneer Sir Patrick Head, the team has won nine Constructors’ Championships, in association with Cosworth, Honda and Renault. Its roll call of drivers is legendary, with its seven Drivers’ Championship trophies being lifted by true icons of the sport: Alan Jones, Keke Rosberg, Nelson Piquet, Nigel Mansell, Alain Prost, Damon Hill and Jacques Villeneuve. The team has made history before and is out to make it again with a long-term mission to evolve and return to the front of the grid.

Additional Information

#LI-KW1

Atlassian Williams Racing is an equal opportunity employer that values diversity and inclusion. We are happy to discuss reasonable job adjustments.


#J-18808-Ljbffr

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.

Careers Jobs

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.

Jobs

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.

Education Jobs

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.