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Tasks:
• Design and prototype scalable data architectures, including databases, data lakes, and data warehouses as managed services in the private cloud (i.e. K8s based).
• Design and prototype functionalities for these managed services, such as backup/restore, IAM, observability integration etc. and update/upgrade schemes
• Design and specify managed services adhering to product specification, as well as SLAs
• Design and prototype Kubernetes-based deployment strategies for scalable, highly reliable, and manageable data technologies.
• Evaluate different Data technologies options for finding the best basis for managed services
• Design and prototype cross-cutting aspects across different Data services in a consistent and coherent way
• Collaborate with Services, DevOps and Infrastructure teams to optimize data technology deployment processes within a Kubernetes environment.
• Document best practices for knowledge sharing and future reference
• Work closely with engineering team to ensure proper implementation of defined architecture, alignment on tech stack decisions, compliance with architecture standards

Ihre Qualifikationen:
• Proven hands-on software development experience
• Proficiency in data processing languages such as SQL, Java, Python or Scala
• Deep K8s skills and experience, deep Data-speciifc K8s skills and experience (e.g. k8s operators’ development experience and/or k8s operators for Big Data technologies)
• Knowledge and experience with the Data technologies/frameworks: RDBMS (PostgreSQL/MySql etc.), NoSQL Storages (MongoDB, Cassandra, Neo4j etc.), Timeseries (InfluxDB, OpenTSDB, TimescaleDB, Prometheus etc.), Workflow orchestration (AirFlow/Oozie etc.), Data integration/Ingestion (Flume etc), Messaging/Data Streaming (Kafka/RabbitMQ etc.), Data Processing (Spark, Flink etc.)
• And/Or with their Cloud provided counterparts, i.e., Cloud Data/Analytics services (GCP, Azure, AWS)
• Knowedge of Data technologies not only just from usage but also from deployment prospective and experince with on-prem deployments
• Knowledge and experience with reference Big Data architectures (Warehouse, Data Lake, Data Lakehouse) and their implementation.
• Experience in implementing and operating data intensive applications
• Strong focus on DataOps/DevOps, In-depth knowledge of best practices in data privacy and data protection, Proven experience with DataMesh principles in practice, Data platform development and/or operations experience, Knowledge and experience in lifecycle management in Data (e.g. CD4ML, MLOps, …)

 Advantages:
• Continuous support during the assignment
• Dynamic and innovative market environment
• Fascinating, innovative environment in an international atmosphere
• We help you gain a foothold in innovative companies

Duration: 3 MM++

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