Senior Data Engineer – Data Analytics Platform - (Contract through our external payroll partner with immediate start for 12 months with possible extension)

Design, develop, and maintain scalable data pipelines, data products, and backend services supporting real-time decisioning, reporting, analytics, and AI use cases Design and implement data models for analytical and operational workloads, ensuring performance, scalability, and maintainability. Produce high-quality, secure, testable, and maintainable code following engineering best practices Develop and maintain data governance, data quality, and monitoring solutions across the platform Collaborate with cloud, security, and architecture teams to ensure compliance with enterprise standards and cloud best practices

At Julius Baer, we celebrate and value the individual qualities you bring, enabling you to be impactful, to be entrepreneurial, to be empowered, and to create value beyond wealth. Let’s shape the future of wealth management together.

In our team, you will be a key contributor to building our Data Platform as a foundation for Data Analytics and AI.

• Bachelor’s or Master’s degree in Computer Science, Data Analytics, Information Systems, or a related technical field; or equivalent training and professional experience

• 5+ years of experience designing and implementing data engineering solutions and enterprise-scale data platforms

• 5+ years of experience building ETL/ELT pipelines, data warehouses, and modern data platform architectures

• 5+ years of experience developing and maintaining data applications and services using Python, Scala, or similar languages

• 5+ years of hands-on SQL experience, including performance optimisation, data analysis, and troubleshooting

• 3+ years of experience with cloud data technologies, preferably Databricks on Azure

• Strong hands-on experience building and optimising large-scale data processing solutions using Databricks and Apache Spark (PySpark, Spark SQL, Structured Streaming, Delta Lake)

• Experience working with structured, semi-structured, and unstructured data across a variety of data stores, including data warehouses, relational databases, object storage, and distributed data platforms.

• Solid understanding of data modelling principles

• Experience implementing CI/CD pipelines and automated deployment practices

• Strong design, implementation, and testing skills

• Excellent communication and collaboration skills, with experience working in Agile delivery environments

• Strong experience with Infrastructure as Code using Terraform, including deployment, configuration, and management of Azure and Databricks resources

• Experience with event-driven architectures and streaming technologies, including Kafka, Azure Event Hubs, Event Grid, Apache Flink, or similar

• Experience with DBT and modern data transformation frameworks.

• Experience with Data Vault 2.0 modeling methodologies

• Ability to provide technical leadership and guide architectural and engineering decisions