Data & Automation Engineer

Zurich Insurance Company Ltd.•Waterloo, ON

9d•CA$70,000 - CA$105,000•Hybrid

About The Position

Zurich Canada is seeking a Data and Automation Engineer to join their Actuarial Pricing team. This role is responsible for developing data solutions to support actuarial analyses, enhance business insights, automate data processes, and create reporting tools for trend detection and KPI monitoring. The position offers hands-on experience with actuarial and insurance data using modern tools like Databricks, Microsoft Azure, Python, SQL, Power BI, and AI tools, within Zurich’s established governance, security, and risk standards. This is a new position within the team, offering an opportunity to shape the role and grow expertise in a supportive environment. Zurich Canada utilizes AI-enabled tools in its recruitment process, but all hiring decisions are made by qualified professionals.

Requirements

Bachelors degree in Computer Science, Engineering, Information Systems, or a related discipline
3–6 years of experience in data quality, data governance, analytics, or data management roles.
Hands-on experience with Python for data processing, automation, and workflow improvement.
Strong working knowledge of SQL for querying, transforming, reconciling, and validating large datasets.
Experience with Databricks, Delta Lake concepts, or similar cloud data platforms used to build scalable data pipelines and analytics-ready datasets.
Familiarity with Microsoft Azure services and modern cloud data architecture concepts.
Experience developing dashboards, reports, or business intelligence solutions using Power BI or similar visualization tools.
Hands-on experience using AI tools (e.g. Gemini, Claude, ChatGPT) in data-related projects.
Understanding of data engineering fundamentals, including ETL / ELT, data modeling, schema design, data quality controls, and performance optimization.
Ability to translate business and analytical requirements into practical data solutions that are reliable, reusable, and easy to understand.
Strong problem-solving skills with the ability to investigate data issues, reconcile results, and communicate findings clearly to technical and non-technical stakeholders.
Ability to write clear, maintainable code and follow team standards, documentation practices, and version control processes.
Strong written and oral communication skills to be able to explain complex data engineering concepts to a less technical audience.
A collaborative mindset, open cross-team teaching, and knowledge sharing.

Nice To Haves

Experience working with actuarial, insurance, underwriting, claims, finance, or portfolio management data.
Exposure to pricing, profitability, rate monitoring, loss ratio, catastrophe, or portfolio KPI reporting processes.
Experience with AI-assisted or automated data quality tools, such as automated profiling, anomaly detection, or rule suggestion.
Experience with Delta Lake, Lakehouse architectures, CI / CD concepts, or automated deployment practices for data pipelines.
Familiarity with data governance, privacy, controls, or regulated data environments such as financial services or insurance.
Interest in applying automation, analytics, and AI-enabled capabilities to improve actuarial insight generation and business decision-making.

Responsibilities

Design, build, and maintain data pipelines using Databricks, Microsoft Azure, Python, SQL and the latest AI tools to support actuarial pricing, portfolio management, and business reporting needs.
Automate existing data ingestion, extraction, transformation, and loading processes to improve efficiency, reliability, and scalability across actuarial workflows.
Create analytics-ready datasets that support pricing analyses, trend studies, profitability reviews, rate monitoring, catastrophe load analysis, and portfolio KPI reporting.
Build and maintain Power BI dashboards and automated reports that help actuaries, underwriters, finance partners, and business leaders detect emerging trends and proactively manage portfolio performance.
Partner with actuaries and business stakeholders to understand analytical requirements and translate them into practical data solutions, reporting tools, and repeatable processes.
Streamline processes for extracting, transforming, reconciling, and manipulating data used in actuarial models, analyses, and insights.
Identify, document, and track data quality issues, including root cause analysis, remediation status, and opportunities to use AI-assisted techniques to improve issue detection, triage, and resolution.
Support production processes, including monitoring automated jobs, troubleshooting failures, resolving data issues, and implementing sustainable fixes.
Collaborate with data engineering, analytics, technology, and governance teams to align actuarial data solutions with enterprise standards, controls, privacy requirements, and security expectations.
Collaborate with analytics, data science, and AI teams to enable downstream reporting and Agentic AI / AI-assisted analytics use cases.
Contribute to technical documentation, runbooks, and knowledge sharing to improve continuity, transparency, and adoption of actuarial data solutions.