[C3I] Product Manager/Technical Product Owner (Data Platform & AI Readiness)
Company: Software Mind
Location: Kraków, Lesser Poland Voivodeship, Poland (Remote)
Type: Full-time
Remote: Yes
Posted: 2026-06-24
About this role
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.
Project – the aim you’ll have
We are looking for a technically oriented Product Manager / Technical Product Owner who will co-own the development of a modern data platform enabling analytics, AI, and machine learning use cases.
This role is hands-on and requires understanding of data architecture, data engineering practices, and platform design. You will work closely with Data Engineers, Architects, and ML teams to shape a scalable, reliable, and production-ready data ecosystem.
Profile we’re looking for:
- Technically hands-on mindset (not a purely business Product Manager)
- Able to “speak both languages” – engineering and product
- Interested in building data platforms, not only managing backlog
- Comfortable working close to architecture and implementation topics
Position – how you’ll contribute
- Co-define and evolve the data platform roadmap in collaboration with architecture and engineering teams
- Translate technical and business requirements into epics, user stories, and technical backlog items
- Work closely with Data Engineers and Architects on:
1. data models and architectures (batch/streaming)
2. data pipelines and ingestion frameworks
3. storage (e.g. data lake / data warehouse) and processing layers
- Support design and implementation of platform components for:
1. machine learning workflows (MLOps, feature stores, model lifecycle)
2. data observability, lineage, and quality monitoring
- Ensure datasets a...