Senior Data Engineer - Enterprise Integration & Data Platform (f/m/d)

<p><strong>Mission Brief</strong></p> <p>At Isar Aerospace, we are building the technology and infrastructure needed to make access to space more affordable, sustainable, and scalable.</p> <p>We are looking for a highly skilled, hands-on <strong>Senior Data Engineer</strong> to help design and build the foundation of our enterprise data platform.</p> <p>As our organization grows, so does the complexity of our systems and the data they generate. You will play a key role in connecting our enterprise applications, engineering systems, and operational data sources, enabling reliable data exchange and creating a unified foundation for analytics, automation, and internally developed software applications.</p> <p>You will work with systems such as SAP, Siemens Teamcenter, and other enterprise platforms to design scalable data pipelines, integration services, and a modern data lake architecture.</p> <p>This is a foundational engineering role with significant technical ownership. You will have the opportunity to help define our data architecture, establish engineering standards, and build the infrastructure that enables our teams to access and utilize data across the organization.</p> <p>We are looking for someone who enjoys solving complex integration challenges, is comfortable working with heterogeneous data sources, and combines strong engineering fundamentals with a pragmatic, hands-on approach.</p> <p><br><strong>Your role in our Space Mission...</strong></p> <p><strong>Data Platform Architecture & Development</strong></p> <ul> <li>Design, develop, and maintain scalable data pipelines connecting enterprise applications, engineering systems, and operational data sources.</li> <li>Help establish the architecture and technical foundation of a centralized enterprise data lake or lakehouse.</li> <li>Independently build reliable ingestion, transformation, and data processing workflows supporting both batch and event-driven use cases.</li> <li>Design data storage and processing layers that enable efficient access to structured, semi-structured, and unstructured data.</li> <li>Define data models, transformation patterns, and data organization standards to ensure consistency across systems.</li> <li>Implement monitoring, error handling, logging, and automated recovery mechanisms to maintain pipeline reliability.</li> <li>Evaluate and recommend appropriate data technologies, frameworks, and architectural patterns.</li> </ul> <p><strong>Enterprise Systems Integration</strong></p> <ul> <li>Develop and maintain integrations with enterprise applications, including SAP ERP, Siemens Teamcenter PLM, and other internal platforms.</li> <li>Work with APIs, databases, file-based interfaces, messaging systems, and enterprise integration technologies to enable reliable data exchange.</li> <li>Extract, transform, and harmonize data from heterogeneous systems while preserving data integrity and traceability.</li> <li>Develop reusable connectors, integration components, and data services to accelerate future integrations.</li> <li>Collaborate with enterprise application owners to understand underlying data models, interfaces, and business processes.</li> <li>Support appropriate data synchronization and interoperability between enterprise systems while respecting system-of-record ownership.</li> </ul> <p><strong>Data Engineering & Accessibility</strong></p> <ul> <li>Build data pipelines and curated datasets supporting internal applications, analytics, dashboards, and operational reporting.</li> <li>Develop reusable data models and services that allow teams to access information across multiple systems.</li> <li>Establish data quality checks, validation rules, lineage tracking, and metadata management practices.</li> <li>Support the development of a consistent enterprise data foundation connecting engineering, manufacturing, supply chain, finance, and business operations.</li> <li>Enable downstream use cases including business intelligence, advanced analytics, AI/ML, and process automation.</li> <li>Optimize data processing performance, storage efficiency, and pipeline reliability as data volumes and organizational needs grow.</li> </ul> <p><strong>Engineering Excellence & Collaboration</strong></p> <ul> <li>Establish best practices for data engineering, including version control, automated testing, CI/CD, documentation, and infrastructure automation.</li> <li>Collaborate closely with software engineers