Bede Abbott-White

Full-stack software and cloud engineer with a background in data management, CI/CD implementation, and real-time analytics. Skilled in AWS, Azure, GCP, and full-stack development. Committed to driving efficiency and securing optimal user experience. Eager to explore opportunities to apply my technological expertise and knowledge from motorsports and insurance, to address challenges and drive innovation.

Full-Stack Software Developer at Jaguar TCS Racing (August 2022 - Present)

• Collaborated closely with Performance and Race engineers to develop and maintain an in-team app, enhancing data visualisation for improved decision-making in a dynamic race setting. Utilising the MEAN stack, I managed both the client and server-side as a full-stack developer, devising solutions through services, components, and APIs, and accessing databases and cloud services.
• Designed and implemented a CI/CD pipeline for app deployment on AWS CodePipeline, integrating services such as Elastic Beanstalk, S3, and AWS Lambda, bolstered with unit testing with Jest and E2E testing via Puppeteer.
• Conducted real-time technical analyses of race data during high-pressure race weekends, comparing our drivers' lap times to optimise performance against competitors.
• Operated within an agile team, leveraging Azure DevOps for task management to ensure efficient delivery of software improvement and new functionalities.

• Acquired comprehensive Cloud Engineering expertise, including multi-platform scripting, automation, DevOps pipelines, and deployment on Multi-Cloud (Azure, GCP & AWS) platforms.
• Worked collaboratively in agile teams of up to 5 members, efficiently delivering project milestones, cloud migrations, and presenting agile data project outcomes to stakeholders.
• Demonstrated proficiency in executing high-volume deployments via Azure DevOps CI/CD, implementing automated auditing/testing, and managing cloud costs.

• Transitioned AXA's data pipeline from Kedro to Databricks Jobs, facilitating scheduled data ingestion and enhancing debugging visibility through dependency-based orchestrated multi-task DAGs.
• Introduced and managed a Git Flow model for pull requests, incorporating version control and a YAML CI pipeline tied to Databricks, ensuring rigorous unit testing and linting safeguards for production code.
• Conducted comprehensive debugging and data reconciliation within Databricks notebooks using Python and Pyspark, ensuring dashboard accuracy. The introduction of a severity-ordered node failure table integrated into Power BI led to quicker resolution times and significantly reduced the risk of major failures.
• Established thorough documentation of all code and processes, including a continuous update page detailing node failures and resolutions. This approach enhanced the team's efficiency, allowing for streamlined debugging.

Imperial College London (September 2018 - June 2021)

BSc in Geophysics - Upper Second Class