Senior Data Engineer - Sports Analytics

Senior Data Engineer Salary: £60-75k (plus very attractive bonus on top) Location: London/Leeds (hybrid working or fully remote an option, if preferred) *We are looking to fill 2 roles. Please only apply if you have a strong interest in sports analytics/betting, you will be asked questions on this* Purpose of role: We have an exciting opportunity for a Senior Data Engineer to join a rapidly expanding Sports Analytics company.
The purpose of the role is to architect, design, implement and maintain data infrastructure that facilitates data-driven decision making, innovation and operational efficiency while ensuring that the data pipeline is secure, reliable, and scalable. The role holder will build and maintain high-performance data systems that are foundational to driving business growth and success.
Experience and knowledge: Significant experience in building, maintaining, and scaling large-scale data systems. Knowledge of data privacy and security, including data encryption and masking techniques. Experience with data modelling and architecture design using tools and techniques. Key responsibilities: Designing and implementing scalable data architectures and systems. Developing and maintaining ETL (Extract, Transform, Load) pipelines. Managing data storage, backup, and recovery mechanisms. Writing complex SQL queries to extract data for analysis. Developing and implementing data security. Mentoring and coaching junior data engineers. Interfacing with clients, end users, and business stakeholders to provide transparency and receive guidance on projects, deliverables, and support. Skills and competencies: Essential: Understanding of data modelling concepts and be able to design data models that are optimised for different user cases. Good understanding of distributed systems such as Hadoop, Spark, and Kafka, and should be able to design and implement data pipelines that run on these systems. Write clean, efficient, and maintainable code. Proficient in writing complex SQL queries and have a deep understanding of database design principles. Ability to debug and optimize failing or slow data pipelines and queries. Systems integration experience: networking, data migrations, API integration and design. Enthusiasm for clean systems, including documentation, logging, and reproducibility. Experience with cloud platforms such as AWS, Google Cloud, or Azure and be able to design and implement scalable and secure data architectures. Excellent communication and teamwork skills that allow the engineer to collaborate with stakeholders. The ability to identify data quality issues and implement data quality rules and techniques to improve data accuracy. Interest in sports analytics and/or sports betting. Desirable: Able to troubleshoot complex problems that arise during the Data Engineering process and be able to find effective solutions. Communicate complex technical concepts to non-technical stakeholders and be able to work effectively with cross-functional teams. An understanding of data governance and regulatory compliance requirements, such as GDPR and CCPA, and the ability to ensure that data pipelines meet these requirements. NoSQL experience such as MongoDB Coding in scripting languages such as Python #J-18808-Ljbffr