Senior Data Engineer

As a Data Platform Engineer (Senior) with a focus on data engineering, you will design end-to-end architectures for data platforms. Instead of building individual ETL pipelines, you establish platforms on which teams can define their own pipelines. You focus on scalability, reliability and self-service and select suitable Azure tools.

Tasks & Responsibilities

You design end-to-end architectures, establish self-service platforms and implement governance and cost optimization standards. You select Azure technologies, conduct code reviews and coach the team. You will develop end-to-end data solutions on Azure - from source system connection to processing and provision of data for BI/AI applications. You will select and integrate the appropriate technologies and tools for our customers (e.g. streaming with Kafka/Event Hubs, storage in Azure Data Lake/Azure SQL, processing with Spark). Here you will implement our best practices for coding, testing and monitoring data pipelines to ensure reliability and scalability.

Establishes automated CI/CD workflows and monitors the operational processes (monitoring, alerting) of the platform.

You will work closely with customers and business stakeholders to gather requirements and translate them into technical solutions.

You are part of our pre-sales and consulting activities.

Requirements

• 5+ years of experience in data engineering / data platform development, including extensive practice on Microsoft Azure
• Knowledge of data architectures, distributed data processing systems and cloud services (Azure Data Factory, MS Fabric, Databricks, Event Hubs etc.)
• Experience in data security and governance - knows data protection requirements and implements access authorizations, encryption and compliance conformity
• Soft skills: Leadership skills to technically lead a team, as well as clear communication skills with customers (including non-technical contacts).
• Language skills: Very good knowledge of German and English.
• Proven ability to achieve performance and cost optimization with large data volumes
• Proficient in DevOps methods: continuous delivery of data pipelines, infrastructure-as-code (e.g. Terraform/Bicep) and containerized deployments.
• Strategic thinking & consulting: Ability to translate business requirements into data architectures and advise clients.