Senior Cloud Data Engineer

Summary

Join Rackspace Technology, a leading IT services provider for public and private cloud technologies, and contribute to the design and implementation of modern data architectures and data pipelines on cloud platforms (AWS or Azure). You will be responsible for implementing ETL/ELT processes, integrating storage systems, automating data processing, and collaborating with various teams. The role requires experience in data projects with a focus on cloud technologies, proficiency in data processing approaches, and a strong understanding of data security. You will advise clients on data strategy and cloud-native data services. Rackspace offers a collaborative environment and opportunities to work with a diverse team in a German-speaking customer environment. The company is known for its commitment to employee well-being and has been recognized as a best place to work.

Requirements

• Several years of practical experience in data projects with a focus on cloud technologies
• Confident handling of various data processing approaches (batch, streaming, micro-batching)
• Ability to model and implement complex data flows from source to analysis
• Good understanding of data security, access concepts, and scaling mechanisms
• Teamwork, communication skills, and willingness to work in a German-speaking customer environment
• AWS Glue, Azure Data Factory, Apache Airflow
• Kafka, Kinesis, Event Hub
• S3, Azure Storage solutions, Azure Data Lake, Cosmos DB, DynamoDB
• Snowflake, Redshift, Azure Synapse
• Parquet, Avro, JSON
• Python, SQL, Bash
• Terraform, YAML, GitOps
• Dbt, Power BI, QuickSight
• End-to-end encryption (in-transit & at-rest), Identity & Access Management, data classification and access controls

Responsibilities

• Design and implement modern data architectures and data pipelines on cloud platforms (AWS or Azure)
• Implement ETL/ELT pipelines, batch and streaming processing with a focus on scalability and maintainability
• Integrate storage systems, event sources, databases, and analytics platforms
• Automate data processing using Infrastructure and DataOps methods
• Collaborate with data scientists, application and infrastructure teams to implement holistic data solutions
• Advise clients on data strategy, governance, and cloud-native data services