<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p><strong>Company Description</strong> Haensel AMS—Advanced Mathematical Solutions—is a technology and data analytics boutique specializing in innovative machine learning and big data solutions. The company leverages advanced mathematical methods to design tailored data-driven strategies that address the specific needs of each client. Working closely with partners across industries, Haensel AMS turns complex data into actionable insights and measurable value. Team members collaborate in an environment that combines technical excellence, analytical rigor, and practical business impact. This setting offers professionals the opportunity to work on challenging projects with modern technologies and methods.</p><p><br/></p><p><strong>Role Description</strong> As a Mid-level Data Engineer at Haensel AMS, you will design, build, and maintain data pipelines that support advanced analytics and machine learning use cases. In close collaboration with data scientists and analysts, you will develop scalable ETL processes, integrate data from multiple sources, and ensure data is clean, reliable, and well-structured. You will contribute to data modeling and data warehouse design, optimize performance, and monitor data quality in production environments. Your work will include documenting data flows, automating workflows, and supporting stakeholders with data access and tooling. This is a full-time, on-site role based in Berlin, Germany.</p><p><br/></p><p><strong>Qualifications</strong></p><ul><li>Strong data engineering skills, including experience with Data Engineering concepts and building robust data pipelines.</li><li>Proficiency in Data Modeling and designing scalable data architectures to support analytics and reporting needs.</li><li>Hands-on experience with Extract Transform Load (ETL) processes and tools to integrate and transform data from diverse sources.</li><li>Knowledge of Data Warehousing concepts and implementation, including schema design and performance optimization.</li><li>Ability to apply Data Analytics techniques to validate, profile, and interpret data for business and technical stakeholders.</li><li>Proficiency in at least one programming language commonly used in data engineering (e.g., Python, Scala, or Java) and SQL.</li><li>Experience with modern data platforms or cloud ecosystems (e.g., AWS, GCP, or Azure) and related services is beneficial.</li><li>Familiarity with workflow orchestration, version control, and CI/CD practices for data solutions is an advantage.</li><li>Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.</li><li>Strong problem-solving skills, attention to detail, and the ability to work collaboratively in a cross-functional, inclusive team.</li></ul> </div>