Staff Analytics Engineer - Cyber Data Platform

As the Staff Analytics Engineer on the Cyber Analytics team, you will be key in designing, developing, and deploying a scalable data lake and data warehouse solutions and data models that empowers our security users to draw insights and develop reporting capabilities, advanced analytics, machine learning, and GenAI solutions to enhance our security defences. You will lead data modelling and architectural decisions, guide technical direction, and mentor the analytics engineering team to deliver high quality data products. At Tesco, we believe in the power of spending more time together, face to face, than apart. So, during your working week, you can expect to spend 60% of your time in one of our office locations or local sites and the rest remotely. We also recognise that life looks a little different for each of us. Some people are at the start of their careers, some want the freedom to do the things they love. Others are going through life-changing moments like becoming a carer, nearing retirement, adapting to parenthood, or something else. That’s why at Tesco, we always welcome a conversation about flexible working. So, talk to us throughout your application about how we can support. Technical Leadership: Take in charge of the design, development and delivery of cyber data lake and data warehouse solutions, as well as analytics frameworks and processes to provide quality data products for various cybersecurity use cases. Analytics Architecture: Design, build and manage ETLs and data quality process to transform the source data into trusted and curated data products, as well as implementation of Data as a Service (DaaS) model to serve various cybersecurity use cases. Coding and Documentation: Drive the standards for producing high-quality code, conducting thorough peer reviews, and proactively providing constructive feedback to other team members on their code and lead the documentation as a service approach for data models and ETLs documentation. Data Security and Compliance: Implement and enforce data security standard methodologies, ensuring the protection of sensitive information and compliance with relevant regulations. Automation and DevOps: Implement various automations and DevOps practices to streamline the deployment, configuration, and management of data lake and data warehouse solutions. Proven experience in leading the teams to deliver robust data models, analytics frameworks and processes, and providing technical guidance for optimal data product solutions. Solid programming experience with Python/PySpark along with proficiency in SQL Experience building data lake and data warehouse solution on cloud services like Databricks on Azure with Data as a Service (DaaS) model. Solid grasp of data lake and data warehouse concepts, analytics architectural patterns, coding standards, code reviews, version control, and CI/CD practices. Expertise in ETL and ELT frameworks for large scale realtime and batch data processing, with hands-on experience in orchestration tools like Airflow and dbt. Experience implementing enterprise BI tools like Tableau for visualisation and dashboard reporting Familiarity with distributed processing and stream processing frameworks Ability to provide clear input, guide, opportunities to help engineers develop and advance. Knowledge of cybersecurity principles and practices.