Data Products & Management Director - Fintech Engineering (m/f/n)

We are building a scale-up inside InPost. InPost Pay is growing from checkout into a full financial platform, and AI will flow through everything we do. You would join the core leadership team of this new business. The InPost executive team backs it, and you would be one of its key members. Our word is extreme ownership: you own the outcome, not just the function. You would be the first Data & AI leader, starting from a blank page, with one of the few proprietary datasets in European payments where checkout, payment and delivery behaviour meet.

Run data as a business. Build the AI-first engine of a new European fintech.

• 8+ years leading data engineering or ML organisations and 5+ years managing managers or team leads.
• Experienced in financial services, payments, or lending data platforms including lakehouse and feature store.
• Experienced architect of production agentic systems with at least 3 years hands-on experience.
• Skilled in applying agentic approaches across ML lifecycle: feature stores, model iteration, monitoring, and observability.
• Comfortable treating agents as production components including orchestration, guardrails, evaluations, human-in-the-loop, observability, and cost/latency management.
• Experienced in building and leading ML teams in regulated settings.
• Fluent with Databricks and Spark at scale.
• Proficient in English (B2+).
• Able to interact with VP/C-level stakeholders.

Bonus points for:

• Regulated fintech or banking experience.
• Scaling a data function from zero.
• Vendor negotiation experience.
• Multi-jurisdiction architecture knowledge.

• Build agent-first systems with guardrails, evaluations, and observability treated as production components.
• Develop and maintain a state-of-the-art ML and MLOps stack for model building, validation, serving, and retraining.
• Establish one unified data and ML platform combining feature store, decisioning layer, and analytics.
• Own data products including their business case, roadmap, and measurable monetary outcomes.
• Ensure decisioning in real time for credit and risk decisions that are fast, explainable, and auditable.
• Implement governance compliant with GDPR, CCD2, and DORA as a competitive advantage.
• Foster a culture of experiments with measured and value-tied models and decisions.
• Manage budget, team headcount, and accountability for results.
• Lead organisational build-out of Data Engineering & Analytics, ML & MLOps, and Data Product from scratch.
• Report to board-level on platform readiness, governance, and delivery risk.
• Collaborate with staff and leadership on architecture and modeling patterns.
• Own agentic standards for ML including iteration, monitoring, observability, cost, and latency.
• Manage ML platform components: serving, monitoring, retraining, feature store, evaluation, and observability layers.
• Partner with Data Engineering and Credit Risk teams.