Company Description We’re one of the world’s leading online gambling companies, revolutionising the industry since 2000. Founded by Denise Coates CBE, we now employ over 10,000 people and serve over 120 million customers in 26 languages. We empower our employees to push boundaries and explore new ideas, cultivating a culture that celebrates and rewards creativity. This offers employees a wealth of growth opportunities, giving them the opportunity to make a real impact in the world of online gambling. As a forward-thinking company, we’re breaking new ground in software innovation too, redefining what’s possible for our global worldwide. Our focus on In-Play betting has solidified our market-leading position, featuring more than 1.38 million In-Play sporting events a year. With over 750 concurrent sporting fixtures at peak and more live sports streamed than anyone else in Europe (750,000), we handle over 6 million HTTP requests daily and process more than 1.5 million bets per hour at peak. Job Description As a Data Engineer, you will play a key role in creating, migrating, and maintaining regulatory reporting systems on Google Cloud Platform (GCP). The Regulatory Data team is a crucial function of the Data Engineering department, responsible for building and maintaining near real-time and batch reporting systems to fulfil regulatory requirements. The team utilises complex Business logic to transform internal data models into those required by industry regulators and third parties. Using your passion for delivering innovative solutions you will support our continued migration from on-premise SQL Server systems to GCP as the team creates new GCP reporting systems You will support this migration while also developing new cloud-native solutions and exploring AI-driven development and automation to improve efficiency, consistency and reliability across the team's processes. Qualifications - Experience with Google BigQuery. - SQL development experience, with the ability to write complex queries, stored procedures, and performance-tune data-intensive workloads. - Familiarity with GCP services such as Cloud Storage, Cloud Functions, Pub/Sub, or Cloud Composer. - Experience with data pipeline development (ETL/ELT) and orchestration tooling. - Exposure to Infrastructure as Code such as Terraform, and CI/CD pipelines such as Git, GitLab. - Methodical, with high attention to detail and the ability to break down complex requirements into simple solutions. - Interest in AI-assisted development tooling, such as Claude Code. - Ability to work to deadlines in a fast-paced, reactive environment. - SQL Server experience to support legacy systems during migration. Additional Information - Developing and maintaining regulatory data submission systems, both existing on-premise and new cloud-based solutions in GCP/BigQuery. - Building and maintaining automated data pipelines for ingestion, transformation, and loading of regulatory data. - Supporting the migration of legacy SQL Server solutions to BigQuery, working closely with seniors and technical leads on architecture and data modelling. - Implementing data validation, monitoring, and alerting to ensure accuracy, consistency, and reliability of regulatory submissions. - Leveraging AI tooling, such as Claude Code, and contributing to the team's automation strategies to reduce manual effort and improve quality. - Participating in code reviews and adhere to departmental standards and the development process. - Conducting QA to ensure solutions are accurate, efficient, and performant. - Creating and maintaining relevant documentation. - Collaborating with other team members to deliver high-quality solutions within required timeframes. By applying to us you are agreeing to share your Personal Data in accordance with our Recruitment Privacy Notice - https://www.bet365careers.com/privacy-policy At bet365, we're committed to creating an environment where everyone feels welcome, respected and valued. Where all individuals can grow and develop, regardless of their background. We're Never Ordinary, and we're always striving to be better. If you need any adjustments or accommodations to the recruitment process, at either application or interview, please don’t hesitate to reach out.

• Pipeline Development and Optimization: Build and maintain reliable, scalable ETL/ELT pipelines using modern tools and best practices, ensuring efficient data flow for analytics and insights. • Data Modeling and Transformation: Design and implement effective data models that support business needs, enabling high-quality reporting and downstream analytics. • Collaboration Across Teams: Work closely with data analysts, product managers, and other engineers to understand data requirements and deliver solutions that meet the needs of the business. • Ensuring Data Quality: Develop and apply data quality checks, validation frameworks, and monitoring to ensure the consistency, accuracy, and reliability of data. • Performance and Efficiency: Identify and address performance issues in pipelines, queries, and data storage. Suggest and implement optimizations that enhance speed and reliability. • Security and Compliance: Follow data security best practices and ensure pipelines are built to meet data privacy and compliance standards. • Innovation and Continuous Improvement: Test new tools and approaches by building Proof of Concepts (PoCs) and conducting performance benchmarks to find the best solutions. • Automation and CI/CD Practices: Contribute to the development of robust CI/CD pipelines (GitLab CI or similar) for data workflows, supporting automated testing and deployment.

• Pipeline Development and Optimization: Build and maintain reliable, scalable ETL/ELT pipelines using modern tools and best practices, ensuring efficient data flow for analytics and insights. • Data Modeling and Transformation: Design and implement effective data models that support business needs, enabling high-quality reporting and downstream analytics. • Collaboration Across Teams: Work closely with data analysts, product managers, and other engineers to understand data requirements and deliver solutions that meet the needs of the business. • Ensuring Data Quality: Develop and apply data quality checks, validation frameworks, and monitoring to ensure the consistency, accuracy, and reliability of data. • Performance and Efficiency: Identify and address performance issues in pipelines, queries, and data storage. Suggest and implement optimizations that enhance speed and reliability. • Security and Compliance: Follow data security best practices and ensure pipelines are built to meet data privacy and compliance standards. • Innovation and Continuous Improvement: Test new tools and approaches by building Proof of Concepts (PoCs) and conducting performance benchmarks to find the best solutions. • Automation and CI/CD Practices: Contribute to the development of robust CI/CD pipelines (GitLab CI or similar) for data workflows, supporting automated testing and deployment.

• Design and improve the cloud structure (AWS, Azure, or GCP) of our data platform, ensuring it works well for many clients at the same time. • Build a system ready for failures, capable of recovering automatically and keeping things running if there are issues in production. • Create clear APIs, templates, and common tools so internal developers can write code faster and with fewer mistakes. • Monitor and optimize the use of cloud servers and tools (like Kubernetes) to keep the product cost-effective to run. • Identify parts of the system that have become outdated and plan how to improve them step-by-step without stopping daily work. • Review team designs and define the company's technical standards, helping to resolve questions without becoming a bottleneck. • Ensure the platform's structure inherently protects customer data and complies with security rules from the very beginning. • Decide how we measure platform performance so that if something fails, we know exactly what happened within a few minutes. • Build quick prototypes and test new tools before deciding whether to adopt them company-wide.