Staff Data Engineer - Fintech Engineering (m/f/n)

InPost Group; Polska; Hybrid; PLN 300,000 - 540,000; Full time

Benefits offered by this job

Hybrid work

Job summary

InPost Group in Poland seeks an experienced Staff-level Data Engineer to architect and own the feature-store and agentic workflows powering credit decisions.

You will define schema, SLAs, reference patterns, and lead high‑risk, cross‑domain data initiatives in a modern AI‑native stack with a hybrid work model. Warsaw/Kraków office optional.

Qualifications

• 8+ years of data engineering at scale.
• 5+ years in Spark/Databricks or equivalent lakehouse.
• 7+ years in SQL, data modelling, and Python.
• Experience with agentic coding tools, loop engineering and guardrails.
• Knowledge of LLM behavior and trade-offs.
• Staff-level engineer capable of setting standards by influence.
• Strong data-quality, observability, lineage, and GDPR governance.
• Experience with payments/fintech data.
• Streaming (Kafka/Structured Streaming) and CDC
• DBT governance experience.

Responsibilities

• Design and own the feature-store architecture that credit decisioning will depend on.
• Define schema, point-in-time correctness and freshness SLAs.
• Set pipeline patterns, modelling standards and lineage approach.
• Lead high-complexity data initiatives, including cross-domain reuse across regulated entities.
• Design the agent loop (context, tools, evals, guardrails) for reliable, cost-controlled AI workflows.
• Shape data quality and speed of money-making decisions.
• Own architecture of agentic harnesses and loop/graph workflows; set reference patterns.

Skills

Data engineering

Spark/Databricks

SQL

Python

Data modelling

Observability

GDPR governance

LLM awareness

Agentic engineering

Streaming (Kafka)

DBT governance

Leadership by influence

Payments data

Data quality

Tools

Databricks

Kafka

Structured Streaming

Job description

• Full-time

Company Description

Be the Architect the whole data platform is built around.

We are building a scale-up inside InPost, and AI will flow through it. You would be the anchor hire of the data engineering team: the technical authority whose design decisions every other engineer builds on. You'd work directly with the Head of Data Engineering and the Head of ML, with the InPost executive team backing the build. This is an individual-contributor role with organisation-wide scope. Your influence comes from your technical credibility, not your headcount. Our word is extreme ownership.

Job Description

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 ownthe 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.
• Feature-store or ML data serving in production
• Payments, fintech or lending data experience
• Streaming (Kafka/Structured Streaming) and CDC
• DBT governance experience.

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