Job Description 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.