Location: <\/span><\/span><\/b>Munich, Germany<\/span><\/span><\/span>
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<\/span><\/span><\/div>CEPRES<\/span><\/span><\/span><\/b> <\/span><\/span><\/span>is the leading\nand fastest\-growing digital investment platform for private capital markets.\nOur global institutional client base benefits from our award\-winning investment\nsolutions, which are delivered through the CEPRES platform connecting thousands\nof professionals worldwide.<\/span><\/span><\/span><\/span>
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<\/span><\/span><\/span><\/p>CEPRES <\/span><\/span><\/span><\/span><\/b>is headquartered in Munich, with staff in New\nYork, Denver, Chicago, London, Heidelberg, and Singapore. Our team is\nincredibly diverse with over 20 nationalities in 6 different locations\nglobally.<\/span><\/span><\/span><\/span>
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<\/span><\/span><\/p>As a Data Engineer at CEPRES, you will work closely with the\nPrincipal and Senior Data Engineers to build and maintain data pipelines and\ninfrastructure. Alongside core data engineering responsibilities, you'll play a\nkey role in evaluating and annotating AI\-generated outputs related to ETL\nworkflows and data engineering systems, helping ensure the accuracy,\nreliability, and scalability of our data solutions. You'll also collaborate\nwith software engineering teams to help integrate the data layer with other\nplatform components.<\/span>
<\/span><\/span><\/p>What You'll Do<\/span><\/span><\/b>
<\/span><\/span><\/p>- Develop,\n optimize, and maintain data pipelines using SQL and Python.<\/span>
<\/span><\/span><\/li>- Build\n and evaluate AI\-generated responses related to ETL/ELT workflows and data\n engineering systems.<\/span>
<\/span><\/span><\/li>- Assess\n AI outputs for data accuracy, pipeline reliability, scalability, and\n workflow efficiency.<\/span>
<\/span><\/span><\/li>- Assist\n in designing and implementing data systems and pipelines under the\n guidance of senior engineers.<\/span>
<\/span><\/span><\/li>- Maintain\n and optimize existing data structures, ensuring integration with software\n applications.<\/span>
<\/span><\/span><\/li>- Document\n data processes and adhere to data governance standards.<\/span>
<\/span><\/span><\/li>- Support\n the data engineering backlog and contribute to best\-practice\n implementation across projects.<\/span>
<\/span><\/span><\/li>- Participate\n in technical discussions and contribute ideas to improve our data\n architecture.<\/span>
<\/span><\/span><\/li>- Collaborate\n with software engineering teams and the Product team to ensure data\n solutions meet application needs.<\/span><\/span>
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Requirements<\/h3>
Must\-Have Experience<\/span><\/span><\/b>
<\/span><\/span><\/p>- Background\n in Data Engineering, Computer Science, Information Systems, or related\n fields.<\/span>
<\/span><\/span><\/li>- 2\u20134\n years<\/span><\/span><\/b> of experience in Data Engineering (mid\-level).<\/span>
<\/span><\/span><\/li>- Hands\-on\n experience building data pipelines and performing data transformations in\n Python and Pyspark.<\/span>
<\/span><\/span><\/li>- Strong\n understanding of ETL/ELT processes, database systems, and data pipeline\n architecture.<\/span>
<\/span><\/span><\/li>- Solid\n knowledge of SQL (including writing efficient, optimized queries).<\/span>
<\/span><\/span><\/li>- Understanding\n data warehousing, workflow automation, and large\-scale data processing\n systems.<\/span>
<\/span><\/span><\/li>- Familiarity\n with version control (e.g., Git) and CI/CD concepts.<\/span>
<\/span><\/span><\/li>- Ability\n to work collaboratively with engineering teams and fast\-paced environment.<\/span>
<\/span><\/span><\/li>- Good\n communication and collaboration skills.<\/span>
<\/span><\/span><\/li>- Exposure\n to a major cloud platform (AWS, Azure, or GCP).<\/span>
<\/span><\/span><\/li>- Strong\n analytical, troubleshooting, and problem\-solving skills with attention to\n detail.<\/span>
<\/span><\/span><\/li><\/ul>Nice\-to\-Have<\/span><\/span><\/b>
<\/span><\/span><\/p>- Experience\n with Databricks.<\/span>
<\/span><\/span><\/li>- Experience\n with Snowflake.<\/span>
<\/span><\/span><\/li>- Exposure\n to AWS Services (Redshift, S3, Glue, Lambda).<\/span>
<\/span><\/span><\/li>- Familiarity\n with data visualization tools (Power BI).<\/span>
<\/span><\/span><\/li>- Experience\n with Docker, Jenkins, Octopus, or NoSQL databases.<\/span>
<\/span><\/span><\/li>- Experience\n with Azure DevOps / Azure Repo.<\/span>
<\/span><\/span><\/li><\/ul>Our Tech Stack<\/span><\/span><\/b>
<\/span><\/span><\/p>- Databricks<\/span>
<\/span><\/span><\/li>- Snowflake<\/span>
<\/span><\/span><\/li>- SQL\n Server<\/span>
<\/span><\/span><\/li>- AWS\n (Redshift, S3, Glue, Lambda)<\/span>
<\/span><\/span><\/li>- Azure\n DevOps<\/span>
<\/span><\/span><\/li>- Azure\n Repo<\/span>
<\/span><\/span><\/li>- Power\n BI<\/span>
<\/span><\/span><\/li>- Docker<\/span>
<\/span><\/span><\/li>- Jenkins<\/span>
<\/span><\/span><\/li>- Octopus<\/span><\/span>
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Benefits<\/h3>- Career Path in Private\nEquity<\/span><\/span><\/span><\/b> Step\ninto one of the most in\-demand and exciting industries in finance today. With\nus, you'll experience unparalleled opportunities for career growth and\npromotions, receive regular feedback, and benefit from mentorship provided by\nan international team of passionate experts. Be part of a unique growth story\nand take on an exciting, challenging role in a dynamic global environment.<\/span><\/span><\/span><\/span>
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