Databricks Engineer

Vincit; Jyväskylä; On-site; EUR 60,000 - 90,000; Full time

Job summary

Vincit in Jyväskylä, Finland is seeking a Databricks Engineer to join our data team and work across a diverse portfolio of customers.

You'll build data platforms and pipelines from source to lakehouse, using PySpark and SQL. Solutions are shaped with customers, and you may lead the work from high-level guidance to hands-on implementation.

Collaborating with customers and colleagues, you’ll ensure reliable, scalable analytics and contribute to evolving the data strategy on multiple projects.

Responsibilities

• Building data pipelines – designing and developing ETL/ELT processes with PySpark and SQL that move data reliably from source systems into Databricks
• Building lakehouse architectures – planning and implementing Delta Lake and lakehouse solutions that support reliable, scalable analytics
• Monitoring and optimizing pipelines – keeping an eye on pipeline performance and reliability

Job description

Do you enjoy building data pipelines and platforms that just work?

We're looking for a Databricks Engineer to join Vincit and work across a diverse portfolio of customers.

In this role, you'll build data platforms and pipelines of various sizes, all the way from source to lakehouse. Solutions are designed together with the customer, and depending on the project your role can range from high-level steering to hands‑on PySpark and SQL work. Requirements aren't always fully defined at the start, so you take the lead in shaping the solution and driving development work proactively.

What is the job like, really?

Our customer base and projects are broad and diverse, ranging from smaller customers with focused data needs to larger enterprises with complex data platforms. You'll work in both advisory-focused engagements and hands‑on delivery projects where Vincit takes full responsibility for the solution.

Here’s a Snapshot Of Your Key Responsibilities

• Building data pipelines – designing and developing ETL/ELT processes with PySpark and SQL that move data reliably from source systems into Databricks
• Building lakehouse architectures – planning and implementing Delta Lake and lakehouse solutions that support reliable, scalable analytics
• Monitoring and optimizing pipelines – keeping an eye on pipeline