Data Engineer

Zurich Insurance Company Ltd.•Toronto, ON

3d•CA$65,000 - CA$100,000•Hybrid

About The Position

Zurich Canada is currently looking for a Data Engineer to build, operate, and support data pipelines and analytics-ready datasets that power reporting, advanced analytics, and AI-enabled use cases across the organization. Reporting to a Data Platform Leader, you will work closely with senior data engineers, data platform teams, analytics partners, and business stakeholders to ingest, transform, and curate high-quality data using modern cloud and Lakehouse technologies. The position provides strong hands-on exposure to enterprise data platforms while operating within Zurich’s governance, security, and risk standards. This is a unique opportunity to build your knowledge and experience for the future in a supportive environment where your voice matters. 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.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline OR Equivalent practical experience
Hands-on experience with Python for data processing and automation
Working knowledge of SQL for querying and transforming large datasets
Exposure to Databricks (jobs, notebooks, Delta Lake concepts) or similar cloud data platforms
Familiarity with Microsoft Azure services (e.g., storage, compute, identity, networking concepts)
Understanding of data engineering fundamentals: ETL / ELT, data modeling, schema evolution, and performance optimization
Basic understanding of data quality, validation, and monitoring concepts
Awareness of modern analytics, AI/ML, or Agentic AI concepts and how data engineering enables these capabilities
Ability to write clear, maintainable code and follow team standards and version control practices
Strong problem-solving skills and willingness to learn in a complex enterprise environment

Nice To Haves

Experience with Delta Lake or Lakehouse architectures
Exposure to CI / CD concepts for data pipelines
Familiarity with data governance, privacy, or regulated data environments (e.g., financial services or insurance)
Experience supporting production systems in a 24x7 enterprise environment
Interest in AI-enabled analytics, automation, or intelligent agents

Responsibilities

Develop and maintain scalable data pipelines using Databricks on Microsoft Azure, supporting batch and incremental data processing use cases
Implement data transformations using Python and SQL, following established engineering standards and patterns
Support ingestion of data from multiple sources including enterprise applications, files, APIs, and event-driven feeds
Assist in designing and maintaining curated datasets in Lakehouse layers (e.g., raw, refined, curated)
Perform data quality checks, basic anomaly detection, and reconciliations to ensure data accuracy and reliability
Participate in production support activities, including monitoring jobs, troubleshooting failures, and resolving data issues
Collaborate with analytics, data science, and AI teams to enable downstream reporting and Agentic AI / AI-assisted analytics use cases
Adhere to data governance, privacy, and security policies, including handling of sensitive and regulated data
Contribute to technical documentation, runbooks, and knowledge sharing across the Data Management team
Continuously learn and apply new platform capabilities and engineering best practices