Manager, Data Platform and Analytics Engineering

Manager, Data Platform and Analytics Engineering

Permanent Full Time | Manager | North York, ON, Canada

Salary Range: $90,000.00 To $ Annually

This is a hybrid position based out of North York at 243 Consumers Rd — you will work 4 days a week in office and 1 day remote.

About Lifemark Health Group - Stronger Than Ever

At Lifemark, we are a movement in rehabilitation, injury management, disability management, and recovery services. With nearly 400 clinics across Canada, we are guided by a people-first culture and our mission to help individuals, teams, and communities thrive.

When you join us, you are stepping into a leadership role that shapes the future of access to rehabilitation, injury management, and workplace recovery services in Canada.

Your Role

The Manager, Data Platform & Analytics Engineering will lead the rebuild and ongoing evolution of our Azure-based data platform, from raw data ingestion through bronze, silver, and gold data layers. This role provides both technical leadership and hands-on engineering support to ensure our data foundations are scalable, reliable, well-governed, and fit for analytics, reporting, and downstream business consumption.

This person will act as a functional manager for data platform and analytics engineering work. They may not build every component themselves, but they must have the technical depth to guide engineers, challenge design decisions, establish standards, review implementation quality, and contribute directly where needed.

The role will also guide semantic model design and formatting, ensuring that curated data assets are business-friendly, consistently structured, performant, and aligned to enterprise analytics standards.

Your

Key Responsibilities:

• Lead the rebuild and modernization of the Azure data infrastructure, including raw, bronze, silver, and gold data layers.
• Define and guide the architecture, patterns, and standards for data ingestion, transformation, curation, and consumption.
• Ensure the platform is designed for scalability, reliability, security, data quality, observability, and maintainability.
• Partner with data engineering, analytics, product, architecture, governance, and business teams to align platform capabilities with business needs.
• Establish clear engineering practices for data pipelines, data models, environments, deployment, testing, documentation, and operational support.
• Provide hands-on technical guidance across Azure data services, data lake design, orchestration, transformation frameworks, and data pipeline development.
• Support the design and implementation of medallion architecture patterns, including raw ingestion, bronze standardization, silver business logic, and gold consumption-ready datasets.
• Guide the team on data partitioning, schema management, pipeline monitoring, error handling, performance optimization, and cost-conscious platform design.
• Review technical designs and code to ensure solutions are robust, reusable, and aligned with platform standards.
• Help troubleshoot complex data engineering issues and unblock delivery teams when required.

Analytics Engineering & Semantic Modeling

• Lead the development of standards for analytics-ready data assets, including dimensional models, curated tables, metrics layers, and semantic models.
• Guide semantic model formatting, naming conventions, measure definitions, relationships, hierarchies, and usability standards.
• Ensure gold-layer datasets and semantic models are intuitive for analysts, BI developers, and business users.
• Promote consistent definitions of key business metrics and help reduce duplication or conflicting logic across reports and dashboards.
• Partner with analytics and reporting teams to ensure data products are performant, trusted, and easy to consume.

Functional Team Leadership

• Act as the functional lead for data platform and analytics engineering work, setting direction, priorities, quality expectations, and delivery standards.
• Coach and mentor engineers, analysts, and other technical contributors on best practices in data engineering and analytics engineering.
• Translate business and technical requirements into practical platform and data product solutions.
• Balance hands-on…