STAFF ENGINEER (DATA ENGINEERING)

Staff Data Engineer - £95,000-£100,000 + Bonus + extensive benefits - Hybrid! I'm working with a global business who work with many of the largest retailers in the world, helping them to increase traffic and sales through their innovative SaaS product. They're looking for a Staff Data Engineer to join their team, working hybrid, in a company which prides itself on staying up to date with cutting edge technology and providing autonomy and freedom to its employees. The Staff Data Engineer will join the Back-End team to help build out the data pipeline, data store and API layer of the data which will power the various client-facing dashboards and product performance reporting. The ideal candidate has experience building and managing systems that aggregate and distribute data sets to support new product offerings. They are adept at evaluating and selecting the appropriate technologies to develop these data sets. Experience/Skills required: ~7+ years experience designing and building robust, scalable, distributed data systems and pipelines, leveraging open source and public cloud technologies. ~ Experience with big data storage and processing technologies: e.g. DBT, SQL, Redshift, Snowflake, RDBMSs (PostgreSQL/MySQL). ~ Experience with AWS ~ Experience with modern build tools - Jenkins, GitHub etc. ~ Experience with Spring Boot or similar API framework. ~ Experience with scheduling services such as Airflow, Oozie. Responsibilities: Developing Data Pipelines: Create and manage robust data pipelines to ensure seamless data flow across our platforms. Optimising Data Storage and Retrieval: Enhance data storage solutions for maximum efficiency, scalability, and speed. Ensuring System Reliability: Maintain the stability and performance of our data architecture to support high availability and resilience. Collaborating with Cross-Functional Teams: Work closely with engineering, analytics, and product teams to gather data requirements and implement tailored solutions. Troubleshooting and Problem-Solving: Address data-related issues promptly, employing effective debugging and optimisation techniques. Advocating Best Practices: Promote software engineering best practices, focusing on efficiency, maintainability, and robust system design.