Introduction

You build the data pipelines that AI systems for a safe, connected world are built on.

Tasks

• You design and implement scalable data pipelines for ingestion, ETL/ELT, as well as batch and streaming use cases.
• You develop and maintain data models, schemas, catalogs, and feature stores for reliable and discoverable data access.
• You ensure data quality, validation, lineage, and observability for high-performance real-time sources.
• You integrate and operate storage solutions such as object stores, SQL/NoSQL and vector databases, and optimize access patterns for product systems.
• You ensure reproducibility and metadata tracking in ML workflows through experiment and feature lineage tracking.
• You automate CI/CD, monitoring, alerting, and capacity planning for pipelines, thereby ensuring operational reliability.

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Qualifications

• You have several years of practical experience in building production data pipelines and platforms for analytics or ML, including real-time and streaming scenarios.
• You have strong programming and scripting skills in Python and SQL, as well as experience with orchestration tools such as Airflow, Prefect, or Dagster.
• You have experience with cloud storage and processing solutions (Azure, AWS, or GCP), containerization and orchestration, as well as data processing frameworks such as Kafka, Flink, or Spark.
• You have practical knowledge of data modeling, metadata and catalog systems, feature stores, as well as experiment and metadata tracking, and have experience operating and integrating object stores, relational and NoSQL databases, as well as vector data stores for ML workloads.
• You stand out through your ability to work in a team, presentation skills, solution-oriented thinking, analytical thinking, and independence, and work closely with AI/ML and MLOps teams with a strong understanding of products.
• Very good German skills and very good English skills (written and spoken) round off your profile.

Contact and further information