Data Engineer (f/m/x)

Das ist der Job

CEPRES is headquartered in Munich, with staff in New York, Denver, Chicago, London, Heidelberg, and Singapore.

Darum lohnt es sich

Our global institutional client base benefits from our award-winning investment solutions, which are delivered through the CEPRES platform connecting thousands of professionals worldwide. Our team is incredibly diverse with over 20 nationalities in 6 different locations globally.

You'll also collaborate with software engineering teams to help integrate the data layer with other platform components. Collaborate with software engineering teams and the Product team to ensure data solutions meet application needs. Ability to work collaboratively with engineering teams and fast-paced environment.

Our Tech Stack Databricks Snowflake SQL Server AWS (Redshift, S3, Glue, Lambda) Azure DevOps Azure Repo Power BI Docker Jenkins Octopus Benefits Career Path in Private Equity Step into one of the most in-demand and exciting industries in finance today.

With us, you'll experience unparalleled opportunities for career growth and promotions, receive regular feedback, and benefit from mentorship provided by an international team of passionate experts.

Culture That Drives Success Commitment to Our People We care deeply about our team, offering outstanding benefits and opportunities to thrive in a diverse, inclusive, and international workplace. Location: Munich, Germany CEPRES is the leading and fastest-growing digital investment platform for private capital markets.

As a Data Engineer at CEPRES, you will work closely with the Principal and Senior Data Engineers to build and maintain data pipelines and infrastructure.

Alongside core data engineering responsibilities, you'll play a key role in evaluating and annotating AI-generated outputs related to ETL workflows and data engineering systems, helping ensure the accuracy, reliability, and scalability of our data solutions. What You'll Do Develop, optimize, and maintain data pipelines using SQL and Python.

Build and evaluate AI-generated responses related to ETL/ELT workflows and data engineering systems. Assess AI outputs for data accuracy, pipeline reliability, scalability, and workflow efficiency. Assist in designing and implementing data systems and pipelines under the guidance of senior engineers.

Maintain and optimize existing data structures, ensuring integration with software applications. Document data processes and adhere to data governance standards. Support the data engineering backlog and contribute to best-practice implementation across projects.

Participate in technical discussions and contribute ideas to improve our data architecture. Requirements Must-Have Experience Background in Data Engineering, Computer Science, Information Systems, or related fields. 2â 4 years of experience in Data Engineering (mid-level).

Hands-on experience building data pipelines and performing data transformations in Python and Pyspark. Strong understanding of ETL/ELT processes, database systems, and data pipeline architecture. Solid knowledge of SQL (including writing efficient, optimized queries).

Understanding data warehousing, workflow automation, and large-scale data processing systems. Familiarity with version control (e.g., Git) and CI/CD concepts. Good communication and collaboration skills. Exposure to a major cloud platform (AWS, Azure, or GCP). Strong analytical, troubleshooting, and problem-solving skills with attention to detail.

Nice-to-Have Experience with Databricks. Experience with Snowflake. Exposure to AWS Services (Redshift, S3, Glue, Lambda). Familiarity with data visualization tools (Power BI). Experience with Docker, Jenkins, Octopus, or NoSQL databases. Experience with Azure DevOps / Azure Repo.

Be part of a unique growth story and take on an exciting, challenging role in a dynamic global environment. Compensation of attractive package that reflects your skills, contributions, and growth potential. At CEPRES, we're committed to creating an inclusive environment for all.

We celebrate diversity and encourage applicants from all backgrounds, experiences, and perspectives to apply.