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- Tasks: Lead a team to develop innovative data products that drive intelligent decisions at Tesco.
- Company: Join Tesco's Product & Engineering team, a leader in data and AI capabilities.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Inclusive culture with a focus on continuous improvement and talent development.
- Why this job: Make a real impact on business and colleagues through cutting-edge data solutions.
- Qualifications: Experience in data product development and strong technical skills in SQL and Python.
The predicted salary is between 60000 - 80000 £ per year.
The Product & Engineering team is responsible for developing and managing data products that drive intelligent experiences, decisions and actions across the Tesco ecosystem. We work in close partnership with business and technology teams across Stores, Finance, People, Customer, Commercial to enable the seamless build, consumption, integration and expansion of Data & AI capabilities. This is a fantastic opportunity to join our team and play a leading role in defining, building and launching a new type of data products that have a real impact on Tesco business & colleagues. This is a hybrid role with the expectation of being based at our London and Welwyn Garden City offices 3 days a week.
Responsibilities
- 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.
Qualifications
- Experience developing robust data products that are actionable and scalable.
- Expertise in using Spark, Dremio, Teradata or other SQL technologies.
- Strong knowledge and experience of using business intelligence, ETL frameworks and visualisation tools such as Tableau.
- Experience with Python, GitHub, data orchestration tools (Oozie, Airflow) for data wrangling, building data pipelines and building analytical/web interfaces.
- Good understanding of the full data lifecycle and understanding of typical enterprise concerns around data platforms (Governance, Quality, Security).
- Experience creating outputs for both technical and non-technical audiences.
- Ability to manipulate, analyse and synthesise data using different sources to create customer led, data driven products and high impact presentations.
- Highly numerate with a university degree of 2:1 or higher in a quantitative discipline or relevant experience.
We are committed to providing a fully inclusive and accessible recruitment process.
Data Analytics Manager employer: WeAreTechWomen
Contact Detail:
WeAreTechWomen Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Analytics Manager
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data products and analytics projects. This is your chance to demonstrate your expertise in SQL, Python, and all those fancy tools you’ve mastered.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex data concepts in simple terms, as you'll need to communicate effectively with both techies and non-techies.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Data Analytics Manager
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Analytics Manager role. Highlight your experience with SQL, Python, and data product development. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data analytics and how you can contribute to our team. Be sure to mention any relevant projects or experiences that showcase your expertise.
Showcase Your Technical Skills: Don’t hold back on showcasing your technical skills in your application. Mention your experience with tools like Spark, Dremio, and Airflow. We love seeing candidates who are hands-on and ready to dive into data challenges!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at WeAreTechWomen
✨Know Your Data Inside Out
Make sure you’re well-versed in the data products and analytics tools mentioned in the job description. Brush up on your SQL skills, especially with Spark and Dremio, and be ready to discuss how you've used these technologies in past projects.
✨Showcase Your Leadership Skills
As a Data Analytics Manager, you'll need to lead a team. Prepare examples of how you've built and mentored teams in the past. Highlight any initiatives you've taken to foster a culture of growth and inclusion, as this will resonate well with the interviewers.
✨Prepare for Technical Questions
Expect technical questions that assess your knowledge of data pipelines, machine learning, and AI-assisted analytics. Be ready to explain complex concepts in simple terms, as you’ll need to communicate effectively with both technical and non-technical stakeholders.
✨Demonstrate Continuous Improvement Mindset
Discuss how you've driven continuous improvement in your previous roles. Share specific examples of how you've enhanced product quality or reduced delivery times, as this aligns with the responsibilities outlined in the job description.