Zurich Capability Center is currently hiring a Data Engineer - Databricks.

Design, build, and maintain scalable data pipelines using Databricks and cloud-based data platforms.
Develop, optimize, and automate ETL/ELT processes to support analytics, reporting, and machine learning initiatives.
Work closely with Data Scientists, Analysts, ML Engineers, and business stakeholders to understand data requirements and deliver reliable datasets.
Implement data ingestion frameworks from multiple structured and unstructured data sources.
Build and maintain data models, data lakes, and data warehouses that support enterprise analytics needs.
Ensure data quality, consistency, governance, and security across platforms.
Optimize Spark workloads and Databricks environments for performance and cost efficiency.
Support deployment and operationalization of machine learning solutions by providing production-ready datasets and feature pipelines.
Collaborate with Azure, IT, and DevOps teams to implement CI/CD and DataOps best practices.
Monitor, troubleshoot, and continuously improve data platform performance and reliability.

Bachelor's Degree in Computer Science, Engineering, Information Systems, Mathematics, or a related field.
3+ years of experience in Data Engineering, Data Warehousing, or Big Data environments.
Strong programming skills in Python and SQL.
Hands-on experience with Databricks, Apache Spark, and distributed data processing.
Experience designing and developing ETL/ELT data pipelines.
Experience working with Data Lakes and modern data architectures.
Understanding of data modeling concepts including Star Schema and Dimensional Modeling.
Experience working with Git and version control systems.
Strong analytical and problem-solving skills.
English level B2 or higher.

Experience working with Delta Lake and Lakehouse architecture.
Knowledge of orchestration tools such as Airflow, Databricks Workflows, or Azure Data Factory.
Experience supporting Machine Learning use cases and MLOps practices.
Familiarity with MLflow and model deployment processes.
Knowledge of CI/CD, Infrastructure as Code, and DataOps practices.
Experience with streaming technologies such as Kafka or Event Hubs.

Zurich Capability Center is currently hiring a Data Engineer - Databricks.

Design, build, and maintain scalable data pipelines using Databricks and cloud-based data platforms.
Develop, optimize, and automate ETL/ELT processes to support analytics, reporting, and machine learning initiatives.
Work closely with Data Scientists, Analysts, ML Engineers, and business stakeholders to understand data requirements and deliver reliable datasets.
Implement data ingestion frameworks from multiple structured and unstructured data sources.
Build and maintain data models, data lakes, and data warehouses that support enterprise analytics needs.
Ensure data quality, consistency, governance, and security across platforms.
Optimize Spark workloads and Databricks environments for performance and cost efficiency.
Support deployment and operationalization of machine learning solutions by providing production-ready datasets and feature pipelines.
Collaborate with Azure, IT, and DevOps teams to implement CI/CD and DataOps best practices.
Monitor, troubleshoot, and continuously improve data platform performance and reliability.

Bachelor's Degree in Computer Science, Engineering, Information Systems, Mathematics, or a related field.
3+ years of experience in Data Engineering, Data Warehousing, or Big Data environments.
Strong programming skills in Python and SQL.
Hands-on experience with Databricks, Apache Spark, and distributed data processing.
Experience designing and developing ETL/ELT data pipelines.
Experience working with Data Lakes and modern data architectures.
Understanding of data modeling concepts including Star Schema and Dimensional Modeling.
Experience working with Git and version control systems.
Strong analytical and problem-solving skills.
English level B2 or higher.

Experience working with Delta Lake and Lakehouse architecture.
Knowledge of orchestration tools such as Airflow, Databricks Workflows, or Azure Data Factory.
Experience supporting Machine Learning use cases and MLOps practices.
Familiarity with MLflow and model deployment processes.
Knowledge of CI/CD, Infrastructure as Code, and DataOps practices.
Experience with streaming technologies such as Kafka or Event Hubs.

Data Engineer - Databricks

Skills