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

Job description

The mission Build a data-first enterprise. Be the real business value layer of the company, owning both strategy and execution. Agent-first systems. Agents are production components with guardrails, evals and observability, not experiments. A state-of-the-art ML and MLOps stack. Models are built, validated, served and retrained properly, on a governed Databricks lakehouse. One data and ML platform. Feature store, decisioning layer and analytics sit on one foundation. Data products with an owner and a number. Each one has a business case, a roadmap and a measurable monetary outcome. Decisioning in real time. Credit and risk decisions are fast, explainable and auditable. Governance as a speed advantage. GDPR, CCD2 and DORA are built in, so we ship faster than competitors, not slower. A culture of experiments. Every model and decision is tested, measured and tied to value. Every decision and every model carries monetary value, and you will run the function accordingly: budget, headcount, return on investment, and accountability for results. What you'll own: The Data & AI strategy for our fintech build, and its delivery. The organisation, built from scratch: Data Engineering & Analytics, ML & MLOps, Data Product. You set the structure, the bar and who joins. The budget, including platform and tooling spend (Databricks/lakehouse), and the trade-offs you defend at leadership level. Board-level reporting on platform readiness, governance and delivery risk. Work with Staff and Leadership team on architecture and modeling patterns. The agentic standard for ML. You run agentic model iteration and agentic model monitoring and observability, bound to the feature store. You own the reliability, cost and latency of agent loops and graphs in production, and you hire against the bar. The ML platform (serving, monitoring, retraining), the feature store, the agent eval and observability layer, and Data Engineering and Credit Risk partners. You are: A builder-leader. You love data and love building systems around it. Hands-on when needed, especially with AI on board. You understand every subsystem you lead, down to the detail. Someone with 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: lakehouse, feature store, point-in-time-correct data serving. An experienced architect of production agentic systems, including agentic harnesses and loop/graph agentic workflows, with 3+ years of hands-on experience. Skilled at applying agentic approaches across the ML lifecycle: feature stores, model iteration, and model monitoring/observability, Comfortable treating agents as first-class production components: orchestration, guardrails, evals, human-in-the-loop, observability, and cost/latency Experienced in building and leading ML teams in regulated settings. Fluent in Databricks/Spark at scale. At ease with VP/C-level stakeholders. Fluent in English (B2+). Bonus points for: Regulated fintech or banking experience Scaling a data function from zero Vendor negotiation Multi-jurisdiction architecture Why InPost? Seat at the table. Core leadership team, executive backing, extreme ownership. Real scope. First hire, your mandate, your organisation. Real data at scale (17 million app users) that needs to be taken care of and translated into valuable decisions A lean, AI-native organisation where decisions take days or hours and concept of two-way door is well understood. We offer B2B type of contract 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.