I
InPost
Data Engineer
Staff Data Engineer - Fintech Engineering (m/f/n)
On-siteStaffData EngineerJust posted
Role summaryAI-generated
You will design and own the feature‑store architecture that underpins credit decisioning, ensuring schema correctness, freshness SLAs, and cost efficiency. You will lead the most complex, high‑risk data initiatives, building agentic loops and cross‑domain feature reuse to directly drive profitable decision speed.
Skills required
About this role
The mission
Design and own the feature-store architecture that credit decisioning will depend on.
- Schema, point-in-time correctness and freshness SLAs, designed to be reliable and cheap to run.
- Reference implementations. You set the pipeline patterns, modelling standards and lineage approach that others copy.
- The hardest problems first. You lead the highest-complexity, highest-risk data initiatives, including cross-domain feature reuse across regulated entities.
- Agent-first delivery. You design the agent loop (context, tools, evals, guardrails) so agentic engineering is reliable, observable and cost-controlled.
- Data that earns. Your architecture directly shapes the quality and speed of money-making decisions.
You will own the architecture and inner workings of agentic harnesses and loop/graph agent workflows bound to the feature store: feature engineering, point-in-time validation, drift and data-quality observability. You set the reference patterns others reuse.
Qualifications
You are:
- Someone who loves data and loves building systems around it.
- Hands-on first. You design and you build, with AI as a daily tool.
- Experienced with 8+ years in data engineering at scale, and 5+ years in Spark/Databricks or equivalent lakehouse architecture.
- Expert in SQL, data modelling, and Python (7+ years), plus performance and cost engineering
- Experienced with agentic coding tools and loop engineering (3+ years): context and tool design, evals, guardrails
- Curious about advanced agentic harness, loop, and graph engineering, with deep knowledge of LLM behavior and trade-offs
- A Staff-level engineer who sets standards through influence, not authority
- Strong in data-quality, observability, lineage and GDPR-aware governance.
- A technical leader without authority, who sets standards and mentors through influence.
- Fluent in English (B2+).
Bonus points for:
- Feature-store or ML data serving in production
- Payments, fintech or lending data experience
- Streaming (Kafka/Structured Streaming) and CDC
- DBT governance experience.
- CCD2/DORA awareness
Additional Information
Why this one:
- Architectural authority from day one. You set the standard.
- Greenfield, high stakes. Your design decisions last for years.
- A modern stack and a lean, AI-native team.
- Hybrid, ideally one day a week in the Warsaw or Kraków office.
- We offer a B2B Contract
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