About Diageo

Diageo is a global leader in premium drinks, with an outstanding collection of brands across spirits, beer, and wine categories. Our portfolio includes iconic brands such as Johnnie Walker, Smirnoff, Guinness, Captain Morgan, and Tanqueray, among many others. We are dedicated to celebrating life every day, everywhere, through our exceptional products, innovation, and commitment to responsible drinking. With a rich heritage and a passion for craftsmanship and quality, Diageo continues to inspire and delight consumers around the world.
Our ambition is to be one of the best performing, most trusted and respected consumer products company in the world. Being part of the Digital and Technology (D&T) team, you'll chip in to our vision and strategy, which is to create competitive advantage for Diageo, delivering insights and solutions with magnificent quality and speed of execution.

• Product Strategy and Vision: develop and complete a clear product vision and strategy aligned with overall business goals. Conduct thorough market research to find opportunity, customer needs, and competitive landscapes.
• Voice of the customer: understands, shape and address the different functions/regions needs, ensuring the business needs are well understood across D&T and within the data product working group.
• Product Roadmap and Delivery: create and prioritize a product roadmap based on business value, customer feedback, and data-driven insights. Collaborate closely with cross-functional teams to deliver high-quality products on time and within budget.
• Value Creation and Measurement: define, measure, and optimize the value delivered by data products. Establish key performance indicators (critical metrics) and track product performance to inform continuous improvement.
• Cross-Functional Collaboration: build positive relationships with data science, engineering, and business teams to ensure alignment and efficient product development.
• Data Governance and Quality: champion data governance initiatives, ensuring data quality, security, and compliance with relevant regulations. Work closely with central governance team on co-designing and implementing internal data policies.
• Product Launch and Scaling: develop and implement release strategies to successfully launch data products. Drive product adoption and scalability across the organization.
• Data Literacy and Culture: promote data-driven decision making and champion a data-centric culture. Responsible for crafting and maintenance of relevant detailed within encouraged remit.
• Team Leadership: provide leadership and mentorship to agile teams, driving innovation and high performance.

• Data Analytics: validated expertise on data analysis and manipulation. The ability to interpret data and transform it in a significant story if vital.
• Desired: knowledge of statistical modelling and visualisation standard methodologies, UX/UI.
• Data manipulation and transformation: proficiency in SQL is required.
• Desired: knowledge in Python/R applied to data analysis. Data management certifications (DAMA or others).
• Data architecture and modelling: intermediate knowledge of data architecture, modelling and data management principles is critical.
• Desired: database design, systems integrations standard processes.
• Experience with ETL/ELT Processes: intermediate knowledge of data extraction, transformation and ingestion processes.
• Desired: standard processes in sophisticated system integrations and quality principles for data ingestion.
• Machine Learning and GenAI: basic understanding of ML models and familiarity with GenAI principles.
• Desired: previous experiences on those domains, with proven benefits/hands-on.
• Agile principles: sophisticated knowledge in agile methods (Scrum, Kanban, SAFe) and tools (Azure DevOps) for product development.
• Desired: certifications on that area.
• Data governance and quality: sophisticated knowledge in data governance and quality assurance principles. Intermediate knowledge of privacy regulations (GDPR).
• Desired: certifications on that are.
• Cloud Platforms and Data Tools: good familiarity with cloud platforms like AWS, Google Cloud, or Azure, as well as data tools like Tableau, Power BI, and Hadoop/Spark, is helpful for building and deploying data products.
• Desired: good understanding and/or certifications on Azure and Databricks.

• Communication and Storytelling: solid experience on translating sophisticated data point into clear, actionable messages.
• Strategic and Innovation thinking: intermediate experience with innovation methods applied to data strategic execution and solid understanding of data products aligned to business goals
• Collaboration and Influence: strong communication skills are critical for working with cross-functional teams, influence customers holders, and rallying support for product initiatives.
• Problem-Solving and Analytical Thinking: sophisticated ability to break down sophisticated problems into actionable steps to solve business challenges.
• Customer compassion: strong ability to understand other's situations and levels of understanding. The ability to visualise the customer needs and translate this to impactful data products is critical.
• Adaptability and Resilience: need to remain agile and open to received and process feedback, adjusting attitudes, processes, and products to changes in technology, business needs, and market trends.
• Project Management: organizational skills, including time management, team management (directly and/or indirectly) and task prioritization, are essential for owning the demands of product development.
• Leadership and mentorship: inspiring and guiding both technical and non-technical team members, often without direct authority, is key to fostering a high-performance, collaborative environment.

• Master Data Management domain expertise
• Hierarchy management: experience handling different hierarchies, in different granularities used across multiple regions, with local curation needs and ability to drive global alliances.
• Industry standard processes: knowledge of industry standard processes is key and implementing those, driving simplicity is a key factor on multiple dimensions of master data (product, outlet, customer).
• Multi-domain knowledge: experience with different functions (supply, finance, commercial and marketing) is crucial.
• Data quality: driving quality at source is key, identifying and addressing golden record's reliability is crucial for the success of this role.