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Holded

Product Owner (CRM/Projects)

Barcelona, SpainB2B SaaSAI/ML

Our Take

Build intuitive business software for entrepreneurs at Holded.

What they’re looking for

  • Product strategy and roadmap ownership
  • Backlog prioritization and trade-off management
  • Customer and data-driven discovery
  • Agile cross-functional squad collaboration
  • Define success and AI quality metrics
  • Design AI-resistant user experiences

Skills & Focus Areas

  • roadmap
  • backlog prioritization
  • agile
  • user stories
  • discovery
  • A/B testing
  • prototyping
  • AI integration

As posted by Holded

Join the team. Make an impact.

At Holded, we believe that daily admin should never stop a great idea from becoming a success. That's why we create intuitive software to empower anyone who dares to start their own business. Long story short: we want to make business simple.

In order to create cutting-edge products that meet the needs of the sector, talent is essential. We are looking for passion, creativity, and commitment. In return, we offer the same.

The Role

As a Product Owner, you will develop the product vision by working closely with C-level and stakeholders and transmit it to our skilled engineering team. Your main responsibilities will be:

  • Take over the roadmap, decisions and learnings from the outgoing PO, and shape your own read of the product state in your first weeks.

  • Own the strategy and roadmap of the CRM or Projects module, balancing adoption improvements, competitive gap closing, and AI integration.

  • Prioritize the backlog and manage trade-offs between new features, experience improvements, product debt and progressive AI capability integration.

  • Lead discovery through customers and data: interviews, quantitative analysis, prototyping, and A/B experimentation.

  • Partner daily with an existing cross-functional squad (engineering, design, data) in an agile setup, refining user stories and accepting deliverables.

  • Define success metrics (adoption, retention, business impact) and AI-specific quality metrics (accuracy, latency, human fallback).

  • Design experiences that hold up against the probabilistic nature of AI outputs: fallbacks, edge cases, and user expectations.

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