Senior Data Engineer - Streaming Analytics (f/m/d)

WHAT YOU WILL DO

• Architect and operate large‑scale ETL, streaming, and distributed data pipelines for vehicle telemetry and sensor data
• Build scalable, cost‑efficient, high‑throughput pipelines for analytical, streaming and AI workloads
• Evaluate, introduce, and integrate new data technologies to improve scalability, performance, and observability
• Mentor engineers, provide technical guidance, and foster best practices in software and system design
• Drive ambiguous technical problems toward clarity and deliver practical, scalable solutions
• Collaborate closely with cross‑functional teams to ensure reliable, efficient data infrastructure

WHO YOU ARE

• Hold an M.S. in Data Engineering, Computer Science or a related field
• Bring 10+ years of experience in distributed data architectures, especially designing and operating large-scale data systems using Spark or Databricks
• Proven hands-on experience with Kubernetes and Kafka and stream processing frameworks (Kafka Streams, Spark Streaming, Apache Flink)
• You have experience with event-driven architectures and data streaming and a deep understanding of streaming and batch data processing paradigms, including data ingestion from edge devices
• Experience working with geospatial data, vehicle telemetry, or IoT-scale pipelines is a plus
• Strong communication skills and the ability to influence through clear technical reasoning and collaboration
• Demonstrated ability to work independently, self-direct, and lead projects without close supervision
• Fluent English (spoken and written) and basic German skills are required

NICE TO KNOW

• Remote work options
• Temporary work from abroad in selected countries
• Flextime / optional working hours
• Company pension plan
• Annual professional development
• Sabbatical option up to 6 months
• 30 days paid + 10 days unpaid leave
• Possibility for VW Group car leasing
• If you have further questions about the candidate journey at CARIAD, please contact us:

At CARIAD, we embrace individuality and diversity because we believe our differences make us stronger. We actively seek to build teams with a variety of backgrounds, perspectives, and experiences. Our goal is to create an environment where everyone feels valued and empowered to contribute. If you need assistance with your application due to a disability, please reach out to us at - we are happy to support you.

We are CARIAD, the automotive software company of the Volkswagen Group. Our teams build automotive software platforms and digital customer functions for iconic brands like Audi, Volkswagen, and Porsche – supporting the Volkswagen Group in becoming the leading automotive technology company. With CARIDIANS in Germany, the USA, China, Estonia, and India, we are transforming automotive mobility for everyone.

Join us and be part of this exciting journey!

YOUR TEAM

We are building the central data platform for vehicle intelligence across CARIAD and all Volkswagen Group brands and Rivian Volkswagen Technologies (RTV). Our platform enables large‑scale data collection, management, secure provisioning, and Data- and AI‑driven analytics to support advanced data driven development. We work cross‑brand and provide unified data products, a consistent legal and compliance framework, and a shared technology strategy. Our engineering teams develop scalable systems for fleet‑scale telemetry and machine‑learning applications, driving performance, safety, and sustainability of software-defined vehicles . We are now expanding into intelligent data service, streaming analytics and AI Agents. Join us to shape the future of automotive AI together.

WHAT YOU WILL DO

• Architect and operate large‑scale ETL, streaming, and distributed data pipelines for vehicle telemetry and sensor data
• Build scalable, cost‑efficient, high‑throughput pipelines for analytical, streaming and AI workloads
• Evaluate, introduce, and integrate new data technologies to improve scalability, performance, and observability
• Mentor engineers, provide technical guidance, and foster best practices in software and system design
• Drive ambiguous technical problems toward clarity and deliver practical, scalable solutions
• Collaborate closely with cross‑functional teams to ensure reliable, efficient data infrastructure

WHO YOU ARE

• Hold an M.S. in Data Engineering, Computer Science or a related field
• Bring 10+ years of experience in distributed data architectures, especially designing and operating large-scale data systems using Spark or Databricks
• Proven hands-on experience with Kubernetes and Kafka and stream processing frameworks (Kafka Streams, Spark Streaming, Apache Flink)
• You have experience with event-driven architectures and data streaming and a deep understanding of streaming and batch data processing paradigms, including data ingestion from edge devices
• Experience working with geospatial data, vehicle telemetry, or IoT-scale pipelines is a plus
• Strong communication skills and the ability to influence through clear technical reasoning and collaboration
• Demonstrated ability to work independently, self-direct, and lead projects without close supervision
• Fluent English (spoken and written) and basic German skills are required

NICE TO KNOW

• Remote work options
• Temporary work from abroad in selected countries
• Flextime / optional working hours
• Company pension plan
• Annual professional development
• Sabbatical option up to 6 months
• 30 days paid + 10 days unpaid leave
• Possibility for VW Group car leasing
• If you have further questions about the candidate journey at CARIAD, please contact us:

At CARIAD, we embrace individuality and diversity because we believe our differences make us stronger. We actively seek to build teams with a variety of backgrounds, perspectives, and experiences. Our goal is to create an environment where everyone feels valued and empowered to contribute. If you need assistance with your application due to a disability, please reach out to us at - we are happy to support you.

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Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung