Data Engineer

The Opportunity

Are you looking for a caring, collaborative, values-driven workplace with inspiring teammates and leaders? Do you have the ambition and desire to be the best and thrive at the most impactful global insurance provider in the world? Look no further than Zurich Canada.

If you have experience in data engineering and are looking for an opportunity to build your skills on modern enterprise data platforms, we would love to hear from you.
Zurich Canada is looking for a Data Engineer to build, operate and support data pipelines and analytics-ready datasets that power reporting, advanced analytics and AI-enabled solutions across the organization.
Reporting to the Data Platform Leader, you will work with data engineers, platform teams, analytics partners and business stakeholders to ingest, transform and curate high-quality data using Microsoft Azure and Databricks. You will gain hands‐on experience with enterprise data platforms while working within Zurich's data governance, security and risk standards.
This is an opportunity to deepen your technical expertise, contribute to meaningful enterprise data initiatives and continue building your career in a collaborative environment where your ideas and perspective are valued.
This posting is for an existing vacancy.
Zurich Canada uses artificial intelligence–enabled tools to support certain aspects of the recruitment process, including the initial review and screening of applications. Artificial intelligence is not the sole basis for candidate shortlisting or selection. All hiring decisions are reviewed and made by qualified hiring professionals.
Zurich follows a hybrid work model requiring three days per week of in‐person presence, which may include time in the office or market‐facing engagements.

What you will do:

• Develop and maintain scalable data pipelines using Databricks on Microsoft Azure for batch and incremental data processing.
• Build data transformations using Python and SQL, following established engineering standards and patterns.
• Support the ingestion of data from enterprise applications, files, APIs and event‐driven sources.
• Help design and maintain curated datasets across Lakehouse layers, including raw, refined and curated data.
• Perform data‐quality checks, anomaly detection and reconciliations to improve data accuracy and reliability.
• Monitor production jobs, troubleshoot failures and resolve data issues.
• Partner with analytics, data science and AI teams to create trusted data foundations for reporting, advanced analytics and AI‐enabled use cases.
• Follow Zurich's data governance, privacy and security requirements, including standards for sensitive and regulated data.
• Contribute to technical documentation, runbooks and knowledge sharing across the Data Management team.
• Continue developing your technical capabilities by applying new platform features and engineering practices.

Job Qualifications – What you bring to the table

Bachelor's degree in Computer Science, Engineering, Information Systems or a related discipline OR Equivalent practical experience AND

• Hands‐on experience using Python for data processing and automation.
• Working knowledge of SQL for querying and transforming data.
• Exposure to Databricks, including jobs, notebooks and Delta Lake concepts, or a similar cloud data platform.
• Familiarity with Microsoft Azure services such as storage, compute, identity and networking.
• Understanding of core data‐engineering concepts, including ETL/ELT, data modelling, schema evolution and performance optimization.
• Understanding of data‐quality, validation and monitoring practices.
• Awareness of modern analytics and AI/ML concepts and the role data engineering plays in enabling them.
• Ability to write clear, maintainable code and work within version‐control and engineering standards.
• Strong problem‐solving skills, curiosity and a willingness to learn in a complex enterprise environment.

Preferred

• Experience with Delta Lake or Lakehouse architectures.
• Exposure to CI/CD practices for data pipelines.
• Familiarity with data governance, privacy or regulated‐data environments such as financial services or insurance.
• Experience supporting data solutions in an enterprise production environment.
• Interest in AI‐enabled analytics and automation.