1. Understand the business and the client
Understand who are our clients and why they consume our solutions; understand our business model, our unique value proposition and the company strategy; build deep knowledge on how Research teams produce data and how it reaches clients; build relationships with internal users to learn their workflows, constraints and pain points, and the commercial outcomes behind them.
2. Product Discovery
Use your knowledge on the business, on internal and external clients to judge which problems are worth solving and to ground product decisions in evidence; influence prioritization decision-making; autonomously run product discovery and data analysis to identify opportunities; help shape and maintain the roadmap; influence decisions on time vs scope vs resource trade-offs.
3. Technology domain
Apply your in-depth data domain knowledge partnering with architects and engineers to design data models, API contracts, systems integration, data pipelines and platform requirements representing client needs and business objectives. Design requirements for data products that people, systems and AI Agents can find, trust and reuse without rework.
4. Product Execution
Independently lead sessions with stakeholders and line manager to gather, analyse, and document business and technical requirements. Autonomously collaborate with architects, data, platform and software engineers to clarify feasibility and align on the technical solution that addresses those requirements. Coordinate implementation. Provide documentation, training and support to end-users.
Keep engineering, stakeholders and leadership aligned; manage expectations and provide regular updates; Build and maintain strong relationships with stakeholders at all levels; Communicate progress, issues and solutions effectively within the project team and across stakeholders and end-users; Mentor junior team members.