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Senior Data Engineer - Data Platform

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Data EngineerData

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Quick Summary

Key Responsibilities

Further develop, maintain, and operate a modern data platform built on Databricks. Design, develop, optimize, and maintain scalable data pipelines and data products.

Requirements Summary

Further develop, maintain, and operate a modern data platform built on Databricks. Design, develop, optimize, and maintain scalable data pipelines and data products.

Technical Tools

Data EngineerData

As a Senior Data Engineer – Data Platform, you will play a key role in developing and operating a modern, scalable data platform within a Microsoft Azure environment. You will design and optimize data pipelines and data products using Databricks, Python, SQL, and Apache Spark. Working with Data Vault 2.0 and dbt Core, you will ensure reliable data transformations, high-quality data models, and robust validation processes. You will also contribute to automating development and deployment workflows through CI/CD practices. Collaborating with agile project and product teams, you will help deliver efficient, secure, and cost-effective data solutions. This role offers the opportunity to tackle complex data engineering challenges in a dynamic corporate environment.

You will contribute to the continuous improvement and reliable operation of the organization's data platform, ensuring that data solutions remain scalable, efficient, and aligned with business requirements. Your responsibilities will include:

Further develop, maintain, and operate a modern data platform built on Databricks.

Design, develop, optimize, and maintain scalable data pipelines and data products.

Implement robust data models following Data Vault 2.0 principles.

Develop and manage data transformations using dbt Core on Databricks.

Establish and maintain data quality, testing, and validation processes to ensure data accuracy and consistency.

Ensure the stable, high-performance, and cost-efficient operation of data solutions within Microsoft Azure.

Work with historical data models, dimensional modeling, and data documentation practices.

Contribute to agile project and product teams, collaborating with stakeholders to deliver data engineering solutions.

Support the automation of development, testing, and deployment workflows using CI/CD pipelines.

Apply best practices in version control, automated testing, and continuous integration to improve engineering efficiency.

Requirements

~1 min read

The ideal candidate is an experienced data engineering professional with strong technical expertise in modern data platforms, cloud environments, and scalable data architecture. You should be comfortable working independently, solving complex technical challenges, and collaborating within an agile corporate environment. The following qualifications are required:

At least 5 years of professional experience in data engineering.

Strong hands-on experience with Databricks, SQL, Python, Apache Spark, and PySpark.

Proven experience designing, developing, and operating scalable data pipelines.

Advanced knowledge of dbt Core, particularly within Databricks environments.

Solid experience with data modeling based on Data Vault 2.0.

Experience with historical data models, dimensional modeling, data quality testing, and data documentation.

Excellent knowledge of Git, CI/CD pipelines, and automated testing practices.

Practical experience operating data solutions in Microsoft Azure.

Several years of experience working in a corporate environment.

Strong analytical and problem-solving skills, with the ability to independently understand and resolve complex technical issues.

An independent, structured, and goal-oriented approach to work.

High willingness to learn and adaptability in a dynamic environment.

Experience with agile methodologies; knowledge of SAFe is an advantage.

Fluent German and English communication skills.

Additional qualifications that would be an advantage:

- Previous experience in the health insurance sector.

What We Offer

~1 min read

Remote Work: Worldwide remote working opportunity.

Contract Type: Fixed-term employment contract.

Start Date: As soon as possible.

Professional Development: Opportunity to work with modern data engineering technologies, including Databricks, Azure, dbt Core, and Apache Spark.

Technical Challenges: Exposure to complex data architecture, large-scale data pipelines, and enterprise data platform development.

Collaborative Environment: Work alongside agile project and product teams on impactful data initiatives.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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✓Remote Work: Worldwide remote working opportunity.

✓Contract Type: Fixed-term employment contract.

✓Start Date: As soon as possible.

✓Professional Development: Opportunity to work with modern data engineering technologies, including Databricks, Azure, dbt Core, and Apache Spark.

✓Technical Challenges: Exposure to complex data architecture, large-scale data pipelines, and enterprise data platform development.

✓Collaborative Environment: Work alongside agile project and product teams on impactful data initiatives.