Are you passionate about driving value from data and turning it into actionable insights? Join our dynamic data consulting team as a Data Product Analyst, where you will manage data as a product for clients across a wide range of industries. Acting as the bridge between business stakeholders and technical teams, you ensure a shared understanding of priorities, requirements and outcomes while helping to make data products reliable, discoverable and business-driven.
At Datashift, AI helps us do better work. We use it every day to speed up our analysis, find insights faster and add more value for our clients. It is part of how we work, not a separate specialty. We are looking for someone who is keen to do the same: someone who uses AI to improve their own work and helps client data teams adopt AI in a safe and practical way.
The ideal candidate combines strong analytical capabilities with a value-driven and ownership-oriented mindset. You thrive in cross-functional environments, enjoy translating complexity into clarity and are motivated by creating measurable business impact by working closely together with the involved stakeholders.
Key Responsibilities
Understand business needs and challenges, and identify how data can drive impactful solutions. Translate business problems into data-driven questions and leverage AI to accelerate requirements gathering, analysis and documentation.
Identify the optimal data product solution that balances benefit and cost, ensuring it meets business needs effectively.
Perform source-to-target mapping and design semantic data models, partnering with data engineers to deliver scalable, high-quality data products that meet business needs.
Ensure the required source data is captured and meets quality standards by effectively managing data contracts with application owners and process experts.
Manage the data product backlog for a specific data domain, prioritizing tasks based on value and alignment with business goals.
Promote data democratization by enhancing data accessibility and usability through comprehensive documentation, internal marketing and training of the data product catalog.
Oversee the lifecycle and lineage of data products, ensuring they remain accurate, relevant, and aligned with business objectives while continuously exploring AI-driven opportunities to improve data products and ways of working.
