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- Tasks: Design and implement scalable data infrastructure while mentoring junior engineers.
- Company: Join Atlassian Williams Racing, a leader in technology and innovation in motorsport.
- Benefits: Enjoy a full-time role with opportunities for remote work and professional growth.
- Other info: Diversity and inclusion are valued; reasonable job adjustments can be discussed.
- Why this job: Shape the future of data engineering in a fast-paced, innovative environment.
- Qualifications: Bachelor’s or Master’s in Computer Science; hands-on experience in data engineering required.
The predicted salary is between 36000 - 60000 ÂŁ per year.
1 week ago Be among the first 25 applicants Get AI-powered advice on this job and more exclusive features. 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. 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. 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. Atlassian Williams Racing is an equal opportunity employer that values diversity and inclusion. We are happy to discuss reasonable job adjustments. Seniority level Seniority level Mid-Senior level Employment type Employment type Full-time Job function Job function Information Technology Referrals increase your chances of interviewing at Williams Racing by 2x Sign in to set job alerts for “Data Engineer” roles. 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Senior Data Engineer employer: Williams Racing
Contact Detail:
Williams Racing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as Apache Airflow, AWS, and Python. Having hands-on experience or projects that showcase your skills with these tools can set you apart from other candidates.
✨Tip Number 2
Network with current or former employees of Atlassian Williams Racing on platforms like LinkedIn. Engaging in conversations about their experiences can provide valuable insights into the company culture and expectations for the Senior Data Engineer role.
✨Tip Number 3
Prepare to discuss your previous experience in mentoring junior engineers and how you've contributed to engineering culture. This is a key aspect of the role, so having specific examples ready will demonstrate your leadership capabilities.
✨Tip Number 4
Stay updated on the latest trends in data engineering and cloud technologies. Being able to discuss recent advancements or best practices during your interview can show your passion for the field and your commitment to continuous learning.
We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data engineering, particularly with cloud platforms and data pipelines. Use keywords from the job description to demonstrate your fit for the role.
Craft a Compelling Cover Letter: In your cover letter, explain why you are passionate about data engineering and how your skills align with the responsibilities outlined in the job description. Mention specific technologies and methodologies you have experience with.
Showcase Your Projects: If you have worked on notable projects related to data engineering, include them in your application. Describe your role, the technologies used, and the impact of the project to illustrate your hands-on experience.
Highlight Mentorship Experience: Since the role involves mentoring junior engineers, be sure to mention any previous experience you have in guiding others. This can set you apart as a candidate who not only has technical skills but also leadership qualities.
How to prepare for a job interview at Williams Racing
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience with data engineering tools and frameworks like Apache Airflow, dbt, and cloud platforms such as AWS. Highlight specific projects where you've built or optimised data pipelines, as this will demonstrate your capability to handle the technical demands of the role.
✨Understand the Business Context
Since this role bridges technical expertise with business needs, make sure you can articulate how your work in data engineering supports broader business objectives. Familiarise yourself with Atlassian Williams Racing's mission and how data plays a crucial role in their operations and innovation.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Be ready to discuss how you would approach challenges related to data quality, observability, and governance. Use examples from your past experiences to illustrate your thought process and solutions.
✨Demonstrate Mentorship Skills
As mentoring junior engineers is part of the role, be prepared to discuss your previous experiences in guiding others. Share specific instances where you helped team members grow, contributed to a positive engineering culture, or implemented best practices within your team.