[ISO] Data Product Manager

Software Mind

1d•Remote

About The Position

Software Mind develops solutions that make an impact for companies around the globe. Tech giants & unicorns, transformative projects, emerging technologies and limitless opportunities – these are a few words that describe an average day for us. Building cross-functional engineering teams that take ownership and crave more means we’re always on the lookout for talented people who bring passion and creativity to every project. Our culture embraces openness, acts with respect, shows grit & guts and combines employment with enjoyment. The Product Manager – Data Platform & AI Readiness is responsible for defining, owning, and evolving the company’s data platform as a strategic product. This role ensures the data foundation is scalable, trusted, secure, and commercially fit for purpose, while preparing the organization for advanced analytics, AI, and machine learning use cases. The role bridges business, technology, and commercial teams to ensure data capabilities are deliberately built to enable monetisable data products, accelerate time to market, and support decision grade intelligence in regulated, trust critical environments.

Requirements

7+ years of experience in product management roles, with significant exposure to data platforms, analytics, or AI enabled products
Strong understanding of modern data architectures, cloud platforms, and data lifecycle management
Experience working closely with Data Engineering, ML, and Architecture teams
Commercial mindset with the ability to link platform capabilities to customer and revenue outcomes
Excellent stakeholder management, communication, and prioritisation skills
Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field

Nice To Haves

Experience operating in regulated or trust critical industries is highly desirable

Responsibilities

Define and own the data platform product vision, long-term roadmap, and prioritised backlog aligned to the Data Products Strategy
Position the platform as a reusable, scalable internal product that directly enables customer-facing and revenue-generating use cases
Translate strategic objectives into clear product increments, epics, and success metrics, balancing speed, quality, cost, and scalability
Partner with Data Engineering, ML, and Architecture teams to plan and sequence delivery in an agile environment
Define platform requirements to support machine learning, MLOps, feature stores, model deployment, and monitoring
Ensure availability of high-quality, well-governed, feature-ready datasets for Data Science and ML teams
Embed data quality, lineage, observability, privacy, and regulatory requirements into platform design and governance standards
Act as the primary interface between technology teams and non-technical stakeholders, communicating platform capabilities in clear, outcome-focused language