Lead Data Engineer -ML Ops

Next Horizon is here. Fueled by investments in talent and technology, our bold strategy to transform is nearly complete.

At Gore Mutual we’ve always set ourselves apart as a modern mutual that does good. Now, we’re proudly building on that legacy to transform our company—and our industry—for the better.

Our path forward sharpens our focus on business performance, driven by leading technology, innovation and an agile, high-performing culture. With Gore Mutual and Beneva announcing their intent to merge in 2026, we’ll be uniting two well-established, financially strong, and trusted brands to become the strongest mutual insurer in Canada, ensuring Canadians have purpose-driven insurance options for generations to come. Come join us.

This position is responsible for developing and maintaining the necessary infrastructure for deploying, monitoring, and optimizing machine learning (ML) and AI models, and maintaining scalable data infrastructure. It plays a pivotal role in strategizing, designing, and implementing robust data and analytics pipelines, ensuring the scalability and reliability of our ML and AI systems, and collaborating with cross-functional teams to advance our data-driven initiatives.

• Design, build, and maintain pipelines that support the development and maintenance of AI/ML models, data ingestion, transformation, and storage of large-scale datasets.
• Optimize data workflows to ensure efficient data processing and availability.
• Develop and implement best practices for deploying, monitoring, and managing robust data and analytics pipelines and AI/ML models in production.
• Collaborate with data scientists to ensure seamless integration and deployment of AI/ML solutions.

• Define the vision, strategy, and roadmap for data engineering and MLOps/Agent Ops in alignment with the company's goals.
• Ensure the scalability, reliability, and efficiency of AI/ML models and data pipelines.

• Lead and motivate a high-functioning engineering team, providing coaching to empower members and delivery squads.
• Encourage innovation, experimentation, and best practices within the data engineering team.