Mission Brief

At Isar Aerospace, we are building the technology and infrastructure needed to make access to space more affordable, sustainable, and scalable.

We are looking for a highly skilled, hands-on Senior Data Engineer to help design and build the foundation of our enterprise data platform.

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.

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.

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.

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.

Your role in our Space Mission...

Data Platform Architecture & Development

Design, develop, and maintain scalable data pipelines connecting enterprise applications, engineering systems, and operational data sources.
Help establish the architecture and technical foundation of a centralized enterprise data lake or lakehouse.
Independently build reliable ingestion, transformation, and data processing workflows supporting both batch and event-driven use cases.
Design data storage and processing layers that enable efficient access to structured, semi-structured, and unstructured data.
Define data models, transformation patterns, and data organization standards to ensure consistency across systems.
Implement monitoring, error handling, logging, and automated recovery mechanisms to maintain pipeline reliability.
Evaluate and recommend appropriate data technologies, frameworks, and architectural patterns.

Enterprise Systems Integration

Develop and maintain integrations with enterprise applications, including SAP ERP, Siemens Teamcenter PLM, and other internal platforms.
Work with APIs, databases, file-based interfaces, messaging systems, and enterprise integration technologies to enable reliable data exchange.
Extract, transform, and harmonize data from heterogeneous systems while preserving data integrity and traceability.
Develop reusable connectors, integration components, and data services to accelerate future integrations.
Collaborate with enterprise application owners to understand underlying data models, interfaces, and business processes.
Support appropriate data synchronization and interoperability between enterprise systems while respecting system-of-record ownership.

Data Engineering & Accessibility

Build data pipelines and curated datasets supporting internal applications, analytics, dashboards, and operational reporting.
Develop reusable data models and services that allow teams to access information across multiple systems.
Establish data quality checks, validation rules, lineage tracking, and metadata management practices.
Support the development of a consistent enterprise data foundation connecting engineering, manufacturing, supply chain, finance, and business operations.
Enable downstream use cases including business intelligence, advanced analytics, AI/ML, and process automation.
Optimize data processing performance, storage efficiency, and pipeline reliability as data volumes and organizational needs grow.

Engineering Excellence & Collaboration

Establish best practices for data engineering, including version control, automated testing, CI/CD, documentation, and infrastructure automation.
Collaborate closely with software engineers developing internal applications and platforms.
Work directly with enterprise systems, engineering, manufacturing, and business stakeholders to translate data requirements into technical solutions.
Apply appropriate security, access control, and data governance principles to enterprise data.
Leverage modern development tools, including AI-assisted coding, to accelerate implementation, testing, and troubleshooting.
Contribute to the evolution of our long-term enterprise data strategy and architecture.

What you´ll need to thrive...

5+ years of professional experience in data engineering, software engineering, or enterprise data integration.
Strong programming skills in Python and SQL, including the ability to develop production-quality data pipelines and services.
Extensive experience integrating heterogeneous data sources, including relational databases, APIs, and enterprise applications.
Strong knowledge of relational database technologies such as PostgreSQL, MySQL, SQL Server, or comparable systems.
Experience developing ETL/ELT pipelines and implementing data transformation, validation, and orchestration workflows.
Experience with modern data platform architectures, including data lakes, data warehouses, or lakehouses.
Good understanding of data modeling, data quality and metadata management
Experience with workflow orchestration, scheduling, monitoring, and automated data processing.
Familiarity with REST APIs, JSON, XML, and common enterprise data exchange patterns.
Experience using Git, CI/CD, automated testing, and modern software engineering practices.
Demonstrated ability to independently design, implement, and operate reliable data solutions.
Strong analytical skills and the ability to understand complex enterprise data models.
Excellent communication skills in English and the ability to collaborate across technical and business teams.

Nice to have...

Experience integrating enterprise platforms such as SAP ERP and PLM systems (e.g., Siemens Teamcenter)
Familiarity with engineering and manufacturing data structures, including Bills of Materials (BOMs), part revisions, product configurations, and material master data.
Familiarity with lakehouse technologies and open table formats such as Delta Lake or Apache Iceberg.
Experience with change data capture (CDC), event streaming, or message-based integration patterns.
Familiarity with Docker, Kubernetes, infrastructure as code, and cloud-native development.
Experience working with enterprise data security, access management, and governance frameworks.
Experience supporting analytics, machine learning, or AI-ready data infrastructure.
Experience in aerospace, automotive, manufacturing, or other engineering-intensive industries.
Practical experience with AI-assisted engineering tools such as GitHub Copilot, Claude Code, or OpenAI Codex.

Beyond technical proficiency, we´re looking for someone who...