Senior Data Governance Engineer

Recrute Action•Toronto, ON

2d•Hybrid

About The Position

Drive enterprise data governance initiatives within a dynamic insurance environment focused on Azure cloud technologies, automation, and large-scale data platforms. This hybrid Toronto-based opportunity offers exposure to global projects, AI-related initiatives, and collaborative Agile teams supporting data quality, governance, and operational excellence.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Management, or a related field.
6 years of experience as a Data Engineer or in a similar data-focused role.
Strong expertise with Azure cloud services, including Azure Data Factory, Azure Databricks, ADLS Gen2, Azure Synapse, and Azure SQL or SQL Server.
Hands-on experience with Databricks Unity Catalog for governance, lineage, and security management.
Experience with Informatica CDGC for metadata management and lineage.
Strong proficiency in SQL and Python.
Familiarity with Java or Scala for distributed data processing.
Solid understanding of data warehousing, relational data modeling, ETL and ELT development, and cloud-native data engineering patterns.
Experience with DevOps and CI/CD pipelines for automating data workflows.
Familiarity with Power BI for dashboard creation, data modeling, and business intelligence support.
Strong understanding of data security practices, including RBAC, encryption, and data masking.
Experience with automation and scripting using Python, PowerShell, or similar tools.

Responsibilities

Design and implement scalable data governance frameworks.
Build, operationalize, and automate data pipelines using Azure Data Factory and Azure Databricks.
Develop and maintain metadata management and cataloging solutions using Databricks Unity Catalog and Informatica CDGC.
Engineer and enforce data quality controls and monitoring systems.
Advance and maintain end-to-end data lineage using governance tools.
Implement role-based access control, data masking, and privacy controls.
Conduct performance analysis, optimization, and capacity planning for large-scale data processing workloads.
Drive continuous improvement initiatives related to data governance and data engineering.
Collaborate with cross-functional and global teams to align data initiatives with enterprise strategy.
Operate within Agile and Scrum methodologies using Teams, JIRA, and Mural.
Support production release cycles and operational support activities.
Collaborate with Data Stewards to support the data quality lifecycle, including business requirements gathering, technical rule application, and remediation support.