InPost

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

Warsaw/Cracow, Poland

Live, first seen by us 21 days ago

Pay: Pay not listed
Work setting: Not stated
Employment: Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting

What you’ll work on

Full posting

Design the feature-store architecture: schema, point-in-time correctness, freshness SLAs
Lead high-risk data initiatives, including cross-domain feature reuse across regulated entities
Set pipeline patterns, modelling standards and lineage approach others reuse
Design the agent loop (context, tools, evals, guardrails) for reliable, cost-controlled agentic engineering
Own feature engineering, point-in-time validation, and drift and data-quality observability

From the employer’s posting

Schema, point-in-time correctness and freshness SLAs

You lead the highest-complexity, highest-risk data initiatives, including cross-domain feature reuse across regulated entities

You set the pipeline patterns, modelling standards and lineage approach that others copy

You design the agent loop (context, tools, evals, guardrails)

feature engineering, point-in-time validation, drift and data-quality observability

Apply at InPost

Posting record

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Status in our records: Live
First seen by us: Oct 14, 202621 days ago
Recorded sightings: 74
Last seen by us: Nov 5, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 21

Similar jobs usually come down between day 6 and day 54.

At InPost, jobs usually come down by about day 12.

From 11,032 similar postings that came down, and 49 of InPost’s. Coming down is not always a hire.

InPost is hiring

12 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Data Engineer hiring is picking up
Takes jobs down quickly
58% of open jobs were reposted

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
SQL
Databricks
dbt
Kafka

Where the posting names them

- 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

- 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

- Streaming (Kafka/Structured Streaming) and CDC - DBT governance experience. - CCD2/DORA awareness

- Payments, fintech or lending data experience - Streaming (Kafka/Structured Streaming) and CDC - DBT governance experience.

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Demand for Data Engineer roles

New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.

August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025: $139,500
Expected change, 2025 to 2035: 9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

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 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

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.

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