Senior Data Engineer – Data Analytics Platform 80-100% (f/m/d) - (Contract through our external[...]

Julius Baer; Zürich; On-site; CHF 120,000 - 180,000; Full time

Job summary

Julius Baer seeks a skilled Data Platform Engineer to build and maintain real-time data pipelines and backend services powering analytics and AI initiatives.

You will design data models, govern data quality, and implement secure, scalable data solutions using Python/Scala, Spark, Databricks, and Azure. Collaboration with cross-functional teams is essential.

Qualifications

• Bachelors or Master’s in CS, Data Analytics, IS, or related field, or equivalent experience.
• 5+ years in ETL/ELT, data warehousing, BI, and data processing pipelines.
• 5+ years building end-to-end data systems with Python/Scala and SQL.
• Experience with Azure cloud and CI/CD workflows.
• Proficient with Databricks, PySpark, Spark Streaming, and Delta Lake.
• Familiarity with various data stores and data modeling principles (Data Vault a plus).
• Experience with Kafka/Event Hubs and containerized microservices (Kubernetes/Docker).
• Knowledge of dbt and BI tools; strong collaboration and leadership skills.

Responsibilities

• Design, develop, and maintain data pipelines and backend services for real-time decisioning, reporting, and data collection.
• Handle data requirements, modeling, governance, and security initiatives.
• Produce high-quality, well-tested, and secure code.
• Develop processes ensuring data security and data quality.
• Collaborate across teams and provide technical leadership to developers.

Skills

Python/Scala

SQL

Data modeling

Data governance

CI/CD

Databricks

PySpark/Spark Streaming

Azure cloud

Kubernetes

Docker

dbt

BI tools

Data Vault (a plus)

Education

Bachelor's/Master's in CS or related

Tools

Databricks

Azure Data Lake

Spark

Kafka

Terraform

Power BI

Kubernetes

Docker

Helm Charts

Delta Lake

dbt

Job description

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.

YOUR CHALLENGE

• Design, develop, and maintain data pipelines and backend services for real-time decisioning, reporting, data collecting, and related functions


• Data Requirements and Modeling


• Data Management and Transformation


• Produce high-quality, well-tested, and secure code


• Develop and maintain software designed to improve data governance and security


• Develop processes designed to ensure Data Security and Data Quality

YOUR PROFILE

• 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 in ETL/ELT, data warehousing, Business Intelligence, and integrating data processing/workflow management tools into pipeline design


• 5+ years building and maintaining end-to-end data systems using Python, Scala, or similar programming languages


• Solid experience in utilising SQL for data analysis, investigating data issues, diagnosing root causes, and designing effective solutions


• Experience in working with cloud-based data technologies, preferably on Microsoft Azure, within continuous integration and delivery (CI/CD) environments


• Demonstrated experience working with large-scale datasets using Databricks, PySpark, Spark Streaming, and Delta Lake


• Hands-on experience with structured, semi-structured, and unstructured data across various storage systems, including relational databases (RDBMS), data warehouses, in-memory caches, and document databases


• Familiarity with cloud storage solutions such as Azure Data Lake and Blob Storage


• Solid understanding of data modelling principles (experience with Data Vault is a plus) and strong skills in system design, implementation, and testing


• Experience with event-driven architectures (e.g., Kafka, Event Hubs, Apache Flink) and containerised microservices platforms (e.g., Kubernetes, Docker, Helm Charts)


• Knowledge and practical use of dbt (data build tool), experience with BI tools


• Excellent communication and collaboration skills, ability to provide technical leadership to other developers