As an Analytics Manager, you will lead a team of analysts to design, build and optimise high-quality, scalable and secure data products that empower data driven decision making across Tesco. You will combine technical expertise in analytics, engineering and modelling with people leadership, ensuring our data environments, tools and outputs are robust, reliable and aligned with business needs. You will collaborate closely with data science, engineering, product and business teams to deliver impactful data solutions and foster a culture of innovation and best practice.
You will be responsible for:
• Team Leadership: Build, mentor and develop a high-performing team of analysts, fostering a culture of growth, inclusion and technical excellence with a strong emphasis on data and advanced analytics.
• Data Product Development: Lead the design, development and optimisation of scalable, secure and high-quality data pipelines and analytical models, enabling advanced analytics, machine learning and operational use cases.
• Collaboration: Work closely with cross-functional teams, including data science, engineering, product and business stakeholders to translate business needs into robust, data driven solutions.
• Technical Excellence: Promote and enable adoption of Technical Standards and Engineering Effectiveness within development squads.
• Technical Experience: Demonstrate expertise in using SQL (Spark, Dremio), Python, GitHub and data orchestration tools (Airflow, Oozie) for data wrangling, building data pipelines and developing analytical interfaces.
• AI-Assisted Analytics: Bring experience and curiosity towards AI-assisted analytics and machine learning, actively exploring opportunities to integrate AI and ML within data and analytics products.
• Data Governance: Ensure data lineage, cataloguing and access controls are implemented and maintained, supporting compliance, discoverability and ethical use of data.
• Continuous Improvement: Drive continuous improvement (speed of delivery, product quality, reduce number of defects and time to fix) and facilitate innovation in business practices and ways of working.
• Stakeholder Engagement: Communicate complex data and analytics concepts effectively to technical and non-technical audiences, enabling informed decision making.
• Talent Development: Support recruitment, onboarding, and ongoing development of talent within the team. Identify skill gaps and lead targeted upskilling initiatives to enable the team to adopt software engineering best practices, AI capabilities, advanced analytics and automation practices.