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# Data Engineer

- **Company:** Datashift NV
- **Location:** Rotterdam, Netherlands
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Agile Methodology, Microsoft Azure, Continuous Integration, Information Engineering, Data Integration, DevOps, Python (Programming Language), Software Engineering, SQL Databases, Data Logging, Delivery Pipeline, Backend, Google Cloud Functions, Api Design, Key Vault, Databricks
- **Published:** July 2, 2026
- **Apply:** https://nl.indeed.com/viewjob?jk=10ad9b3524d7991f

## About the Role

Do you have strong Python skills, a genuine DevOps mindset, and a track record of shipping things that hold up in production? Do you feel at home across software engineering, data engineering, and operations?

You're a practical engineer who takes pride in what ends up running in production. You care about reliability, security, and building APIs that other teams can count on., * At least 3 years of Python experience and a track record of shipping APIs and backend services. Clean, testable code is your default.

 * You have real dbt experience and use it to build well-structured, maintainable data models. SQL is a given.
 * You know your way around API design, CI/CD pipelines, and testing. Keeping production systems observable and stable is something you have done before.
 * Azure is familiar ground. You have worked with Function Apps, Key Vault, VNets, private endpoints, NSGs, and secure API exposure in production.
 * Comfortable in an Agile setup, working daily with data engineers, platform engineers, and business stakeholders.
 * An interest in what is coming on the Databricks platform, particularly Lakebase and Databricks Apps.

## Description

* Own the full lifecycle of Data Integration. Build them in Python with Databricks, Azure Function Apps, and other Azure services, translating requirements into clean, maintainable code;
 * Keep APIs production ready. Performance, security, access control, observability, and cost all fall in your scope;
 * Move legacy data APIs onto the platform's current architectural standards and add structured logging and alerting so issues surface before users notice them;
 * Own the CI/CD setup in Azure DevOps. Build pipelines that carry code through testing and deployment without manual steps;
 * Work with the Data Engineering & Platform team and engineers across teams and countries to shape, ship, and document integrations end to end;
 * Support the team with data engineering tasks where needed.


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