About the Role:

The Data Product Manager is a pivotal role responsible for driving the strategy, development, and launch of data-driven products. This individual will demonstrate their deep understanding of data and product management to bridge the gap between business needs and technical capabilities. We seek a highly analytical and collaborative leader to define, build, and scale data products that deliver significant business value.

Responsibilities:

• Product Strategy and Vision: develop and complete a clear product vision and strategy aligned with overall business goals. Conduct thorough market research to find opportunities and 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 supervise product performance to advise continuous improvement.

• Cross-Functional Collaboration: build strong partnerships 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 go-live strategies to successfully launch data products. Drive product adoption and scalability across the organization.

• Data Literacy and Culture: promote data-driven decision making and foster a data-centric culture. Responsible for creating and maintenance of relevant documented within expected remit.

• Team Leadership: provide leadership and mentorship to agile teams, driving innovation and high performance.

Technical Skills

• Data Analytics: proven expertise on data analysis and manipulation. The ability to interpret data and transform it into a significant story if vital.
oDesired: knowledge of statistical modelling and visualisation best practices, UX/UI.
• Data manipulation and transformation: proficiency in SQL is required.
oDesired: 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.
oDesired: database design, systems integrations standard processes.
• Experience with ETL/ELT Processes: intermediate knowledge of data extraction, transformation and ingestion processes.
oDesired: standard methodologies in sophisticated system integrations and quality principles for data ingestion.
• Machine Learning and GenAI: basic understanding of ML models and familiarity with GenAI principles.
oDesired: 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.
oDesired: 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.
oDesired: good understanding and/or certifications on Azure and Databricks.

Soft Skills

• 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 crucial for working with cross-functional teams, influencing customers, and rallying support for product initiatives.
• Problem-Solving and Analytical Thinking: sophisticated ability to break down complex 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 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 crucial for managing 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.

Commercial domain expertise

• Revenue growth management: experience with revenue metrics and processes, including different dataset knowledge and connection between them, such as Sell-Out sources, pricing and market trends sources.
• Outlet execution: experience with sales execution tools and processes (assortment metrics, share of space, planograms).
• Commercial master data: experience with different master data elements (product, outlet, customer) and different levels of granularity on each.