Staff Data Engineer

• Kraków
• Hybrid

Job Description

THE OPPORTUNITY IN A NUTSHELL

You A deeply technical data engineer who enjoys designing reliable data systems solving complex architectural problems and raising the engineering bar for data products used at scale.

Role Staff Data Engineer a senior individual contributor role focused on the technical foundations of event-driven data products data contracts schema quality observability and long-term maintainability.

Tech stack Kafka Change Data Capture event modeling schema evolution streaming and batch processing data contracts cloud data platforms such as GCP/BigQuery or Snowflake quality and observability tooling.

Company Vend home of FINN Blocket Tori Oikotie DBA and Bilbasen marketplaces used by millions of people across the Nordics.

Location Kraków hybrid.

Why us You will help shape how high-quality data products are engineered across a large marketplace ecosystem. This is a role for someone who wants to go deep into technical decisions influence architecture across teams and build patterns that make data reliable discoverable and safe to consume.

WHO ARE YOU

You are a senior data engineer who likes complexity - not accidental complexity but the kind that comes with distributed systems domain ownership evolving schemas multiple consumers and production-grade reliability.

You have strong hands-on experience with event-driven architecture. Kafka topic design event modeling CDC patterns schema evolution compatibility streaming and batch pipelines are areas where you can challenge assumptions and guide others toward better technical decisions.

You know that a useful data product is not just data made available. It needs a clear contract stable ownership documentation quality signals observability lifecycle management and a thoughtful approach to change.

You are comfortable working with modern cloud data platforms such as GCP/BigQuery or Snowflake. You understand how data flows from operational systems into analytical and product use cases and where things usually break.

You care about engineering standards. Versioning backward compatibility lineage testing monitoring and incident prevention are part of how you think about good data engineering.

You are not only a strong builder but also a technical multiplier. You can review designs mentor engineers create reusable patterns write clear technical documentation and influence decisions without needing formal authority.

You communicate clearly in English and can explain complex technical trade-offs to engineers architects product teams and platform partners.

WHATS THE JOB LIKE

As a Staff Data Engineer your mission is to make Vends data products technically strong reliable and scalable.

You will work close to product engineering teams data engineers platform teams and technical leaders to design and improve the data interfaces that power analytics experimentation AI operational insights and product decision-making.

This is a hands-on senior IC role. You will not spend your time building dashboards or managing infrastructure. Instead you will focus on the engineering quality of data products: how events are modeled how schemas evolve how data contracts are defined how quality is monitored how breaking changes are avoided and how consumers can trust what they use.

You will be expected to go deep. Some days that means reviewing Kafka topics and event payloads. Other days it means helping a team redesign a data flow defining a pattern for schema compatibility investigating a quality issue or creating reference implementations that other teams can adopt.

WHAT YOU WILL WORK ON

Event-driven data architecture
Design and review event-based data flows Kafka topic structures CDC patterns streaming pipelines and batch integration patterns.

Data contracts and schema evolution
Help teams define stable well-documented interfaces between data producers and consumers. Guide decisions around versioning compatibility breaking changes and deprecations.

Production-grade data products
Ensure that data products are reliable discoverable documented testable observable and ready for downstream consumption.

Quality and observability
Define practical quality checks monitoring lineage and alerting patterns that help teams detect and resolve issues before they affect consumers.

Technical standards and reference patterns
Create reusable engineering guidelines examples and decision records that help teams build better data products faster.

Architecture reviews and technical mentoring
Support teams through design reviews RFCs troubleshooting sessions and hands-on coaching.

Lifecycle management
Help teams manage the healthy evolution of data products over time including ownership changes schema changes consumer impact and retirement of outdated data assets.

YOUR TYPICAL WEEK

You might start the week reviewing a proposed Kafka event model for a product team and identifying risks around schema evolution or consumer ambiguity.

Later you could work with platform engineers to clarify a golden path for publishing data products then pair with engineers on improving observability for a high-impact data flow.

You may spend time documenting a reference pattern for CDC-based data ingestion reviewing a breaking-change proposal helping resolve a data quality incident or mentoring engineers on how to design data contracts that remain useful as products evolve.

COLLABORATION & STAKEHOLDERS

You will collaborate primarily with engineers: product engineers data engineers platform engineers architects and peer technical leaders.

You will also work with product managers and data consumers when technical decisions need to be connected to real use cases but your main value comes from technical depth and engineering judgment.

You will influence without direct authority by providing clarity setting standards creating reusable patterns and helping teams make better architectural decisions.

WHAT SUCCESS LOOKS LIKE

After 6 months
You have reviewed and improved critical data flows identified technical risks introduced stronger standards for event modeling and schema evolution and helped teams establish better quality and observability baselines.

After 1 year
Vend has more reliable better-documented and easier-to-consume data products in your area. Teams use clearer data contracts manage schema changes more safely and rely on reusable engineering patterns you helped define. Data consumers experience fewer surprises fewer quality issues and more trust in the data foundation.

WHAT THIS ROLE IS NOT

This is not a platform operations role. You will not be responsible for running Kafka clusters or maintaining cloud infrastructure.

This is not an analytics engineering role. You will not primarily build dashboards reports or dbt models.

This is not a people management role. You will lead through technical depth architectural judgment mentoring and engineering influence.

This is not an ivory-tower architecture role. You will stay close to real systems real teams and real implementation trade-offs.