Data Product Manager - Consumer

Responsibilities Product Strategy and Vision: develop and execute a clear product vision and strategy aligned with overall business objectives. Conduct thorough market research to identify opportunities, 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 (KPIs) and track product performance to inform 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 execute 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 meaningful story if vital. Desired: knowledge of statistical modelling and visualisation best practices, 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 best practices. Experience with ETL/ELT Processes: intermediate knowledge of data extraction, transformation and ingestion processes. Desired: best practices in complex 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: advanced knowledge in agile methods (Scrum, Kanban, SAFe) and tools (Azure DevOps) for product development. Desired: certifications on that area. Data governance and quality: advanced 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. Soft Skills Communication and Storytelling: solid experience on translating complex data point into clear, actionable messages. Strategic and Innovation thinking: intermediate experience with innovation methods applied to data strategic execution and strong understanding of data products aligned to business objectives. Collaboration and Influence: strong interpersonal skills are essential for working with cross-functional teams, influencing stakeholders, and rallying support for product initiatives. Problem-Solving and Analytical Thinking: advanced ability to break down complex problems into actionable steps to solve business challenges. Customer empathy: strong ability to understand other's situations and levels of understanding. The ability to visualise stakeholder 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.