Staff Data Engineer

InPost; Kraków; Híbrido; PLN 120.000 - 170.000; Jornada completa

Descripción de la vacante

InPost is building a scale-up where AI flows through the data platform. You will anchor the data engineering team as the technical authority influencing all design decisions.

You will work directly with the Head of Data Engineering and Head of ML, with executive backing, in an individual-contributor role with organization-wide scope. You will own the architecture, reference patterns, and the agentic harness loop/workflows, shaping data quality, observability, cost efficiency, and lineage across

Formación

• 8+ years in data engineering at scale.

Responsabilidades

• Own architecture and inner workings of agentic harnesses and loop/graph workflows.

Conocimientos

Data engineering

Spark/Databricks

SQL

Python

Observability

GDPR governance

LLM knowledge

Data quality

Cost engineering

Stakeholder influence

Herramientas

DBT governance

Kafka/Structured Streaming

CDC

Descripción del empleo

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.

The mission

• 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

• Someone who loves data and loves building systems around it.
• 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+).

Hands-on first. You design and you build, with AI as a daily tool.

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.