Manager, Data Platform and Analytics Engineering

This is a hybrid position based out of North York @ 243 Consumers Rd - You will work 4 days a week in office and 1 day a week remote About Lifemark Health Group - Stronger Than Ever

At Lifemark, we're more than a healthcare company - we're a movement. With almost 400 clinics across Canada, we lead the way in rehabilitation, injury management, disability management, and recovery services. United by our purpose, \"Movement to a Better Life,\" we are driven by our people-first culture and our mission to help individuals, teams, and communities thrive. When you join us, you're not just stepping into a leadership role - you're helping shape the future of access to rehabilitation, injury management, and workplace recovery services in Canada. Role Summary:

The Manager, Data Platform & Analytics Engineering will be responsible for leading the rebuild and ongoing evolution of our Azure-based data platform, from raw data ingestion through bronze, silver, and gold data layers. This role will provide 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:

Data Platform Leadership

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. Azure Data Engineering

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 technical delivery with leadership responsibilities, stepping in to design, build, review, or troubleshoot as needed. Help build a high‑performing engineering culture focused on quality, ownership, documentation, reuse, and continuous improvement. What we're looking for:

Strong experience designing and building modern cloud‑based data platforms, preferably on Microsoft Azure. Hands‑on experience with data lake architecture, data pipelines, transformation frameworks, and layered data models such as raw, bronze, silver, and gold. Strong understanding of data engineering principles, including ingestion, transformation, orchestration, data quality, lineage, monitoring, and performance tuning. Experience guiding or leading technical teams, either as a people manager, functional lead, technical lead, or senior individual contributor. Ability to review technical designs and implementations, provide constructive feedback, and set clear engineering standards. Experience with analytics engineering concepts, including curated data models, metric definitions, dimensional modeling, and semantic layer design. Strong communication skills, with the ability to explain technical concepts to both engineering teams and business stakeholders. Ability to operate in a hands‑on capacity while also providing direction, coaching, and technical leadership. Preferred Qualifications:

Experience with Azure Data Lake, Azure Data Factory, Azure Synapse, Azure Databricks, Microsoft Fabric, Power BI, or related Azure analytics services. Experience implementing medallion architecture in an enterprise environment. Experience with CI/CD, infrastructure‑as‑code, environment management, and DevOps practices for data platforms. Experience with data governance, metadata management, access controls, data cataloguing, and enterprise data standards. Experience designing Power BI semantic models, tabular models, or enterprise metrics layers. Experience working in a matrixed organization with multiple business, technology, and analytics stakeholders. Key Success Measures:

A clear, scalable Azure data platform architecture is established across raw, bronze, silver, and gold layers. Data engineering standards are documented, adopted, and consistently applied by the team. Gold‑layer datasets and semantic models become easier to use, more consistent, and more trusted by analytics and business users. Data pipelines become more reliable, observable, maintainable, and cost‑effective. The team receives strong technical guidance while continuing to build its own engineering capability. Business stakeholders have greater confidence in the quality, consistency, and usability of enterprise data assets. Ideal Candidate Profile

The ideal candidate is a hands‑on technical leader who has built modern data platforms before and understands what good looks like. They are comfortable moving between architecture, engineering execution, team guidance, and stakeholder conversations. They can set direction, establish standards, coach others, and still roll up their sleeves to solve technical problems when needed. Inclusion

We are committed to creating an inclusive environment where people from all backgrounds can thrive. Improving inclusion and equity is a collective responsibility. Lifemark promotes equal employment opportunities for all job applicants, including but not limited to those self‑identifying as members of Indigenous communities, newcomers to Canada, women, and visible minorities. Accommodation

Accommodations are available upon request for candidates taking part in any aspect of the recruitment and selection process. Please contact talentatlifemark.ca for assistance. #LI-NC1

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