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Data Engineer - Group

Summary

The Data Engineer will develop and maintain CoreGo's Databricks-based data platform, building pipelines and data models to support analytics and machine learning for event payment and access systems. The role involves managing data infrastructure, ensuring data quality, and collaborating with product teams to integrate data outputs into business processes.

CoreGo is an event technology company powering large-scale events and festivals. Our seamless payment and access control solutions ensure smooth operations and effortless experiences for attendees and organizers alike. Combining technical innovation with deep industry expertise, we help bring ambitious live experiences to life.

Job Description

As a Data Engineer, you'll join a growing team and takeon a substantial role in developing and operating CoreGo's data platform with real influence over development. You'll work closely with product management, software developers, and our existing data/analytics team. It's a chance to make a lasting mark on the technology behind some of Europe's biggest live events.

You'll take ownership of CoreGo's data platform on Databricks — developing it, keeping it running reliably, and growing it as our data and AI needs evolve. Day to day, this includes:

Developing and evolving the platform's ingestion, lakehouse architecture, transformation pipelines, and orchestration

Building and maintaining the data models and pipelines that power analytics, reporting, and machine learning workloads across CoreGo Cloud (payments, access control, network, POS)

Maintaining strong data quality, testing, and CI/CD practices across the platform

Defining and evolving access control, data governance, and security standards (e.g. Unity Catalog)

On the operations side, you'll:

Run the platform in production — monitoring, incident response, and performance/cost optimization (cluster and compute management)

Extend the platform with new data products, pipelines, and use cases as CoreGo's data and AI roadmap grows

Collaborate with product management and developers to integrate data/ML outputs into business processes and customer-facing products

Own data reliability and scalability as usage and event volumes grow

Requirements

Degree in Computer Science, Data Engineering, or another relevant technical field

3+ years of hands-on data engineering experience designing and building data pipelines

Practical experience with Databricks (or an equivalent Spark-based platform), including PySpark and workflow orchestration

Experience with infrastructure-as-code and DevOps practices for data platforms

Experience with cloud platforms, ideally Microsoft Azure

Understanding of data security and governance best practices

Strong analytical thinking, attention to detail, and ability to manage your own workload

Ability to work independently and take initiative, while fitting well within the team

Proficiency in English

  • Work with meaningful, growth-driven solutions— our technology powers seamless payment and access experiences for hundreds of thousands of people at some of Europe's most memorable live events, giving you a dynamic, forward-looking environment to build and grow in.
  • Take on real ownership— you'll have genuine influence over how our data platform evolves, with the trust and space to shape decisions rather than just execute on them.
  • Grow with an international team— you'll collaborate closely with experienced colleagues, contributing to CoreGo's international journey.
  • Enjoy a culture that balances ambition and authenticity— we're professional, driven, and down-to-earth. We take our work seriously, without taking ourselves too seriously.

Practical Information

This is a permanent, full-time position. Start date is flexible and will be discussed individually.

We are looking for a person based in Finland who can work remotely while also being available to visit our Helsinki office on an occasional basis.

Considered an Advantage

  • Databricks Certified Data Engineer Professional (or equivalent certification)
  • Familiarity with ML models and tooling
  • Experience with API- and microservice-oriented architectures

See also

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