Gestionnaire principal, Ingénierie du Data Hub et des Produits de Données /Senior Manager, Data Hub Engineering & Data Products

McKesson•Montreal, QC

2d•CA$97,700 - CA$162,800

About The Position

McKesson Canada is seeking a Senior Manager, Data Hub Engineering & Data Products to lead the evolution of our Enterprise Data Hub service. This individual will be responsible for building and scaling modern data engineering capabilities, enabling the delivery of trusted data products, advancing AI-enabled engineering practices, and driving operational excellence across the data lifecycle. This role sits at the intersection of data engineering, product thinking, Agile delivery, governance, DataOps, and innovation. The successful candidate will lead a multidisciplinary team responsible for delivering trusted, scalable, secure, and reusable data products that support business capabilities across Commercial, Specialty Health, Supply Chain, Enterprise, and Digital domains. Unlike a traditional data engineering leadership role, this position is accountable not only for technical delivery, but also for service ownership, platform evolution, engineering modernization, and adoption of AI-powered delivery capabilities aligned to the Data Hub roadmap.

Requirements

Deep knowledge of data integration, data lakes, data warehouses, data modeling, and data product delivery.
Strong knowledge of CI/CD, DevOps, engineering automation, and Agile delivery methodologies.
Knowledge of AI-assisted engineering, software delivery automation, and emerging AI technologies.
Experience implementing data quality controls, automated testing, monitoring, and observability practices.
Understanding of metadata management, lineage, governance, data cataloging, and data ownership concepts.
Excellent communication, stakeholder management, problem-solving, planning, and organizational skills.
Ability to lead through change and influence both technical and business teams.
At least 7 years of progressive experience in data engineering, data management, data platforms, or related disciplines.
At least 3 years of people management and technical team leadership experience.
Experience delivering enterprise-scale data solutions within complex business environments.
Experience leading cross-functional initiatives involving engineering, architecture, products, quality, operations, and business stakeholders.
Experience supporting operational services with accountability for reliability, quality, maintainability, and continuous improvement.
Experience leading Agile delivery teams and improving delivery maturity and predictability.
Experience using AI-powered technologies to enhance engineering productivity, quality, and operational efficiency.

Responsibilities

Lead, coach, and inspire a team of data engineers and technical specialists.
Foster a culture of ownership, accountability, inclusiveness, innovation, and continuous improvement.
Ensure technical and leadership capabilities are aligned with the future needs of the Data Hub.
Support workforce planning, recruiting, talent development, succession planning, and performance management.
Promote a data product mindset focused on customer outcomes and business value.
Establish and drive operational excellence practices.
Ensure the design, implementation, deployment, and support of enterprise-scale data solutions.
Drive the delivery of scalable data ingestion, transformation, quality, semantic, and consumption capabilities.
Enforce engineering standards, reusable patterns, architecture guardrails, and development best practices.
Collaborate closely with business and technology stakeholders.
Drive innovation through adoption of emerging technologies, optimization of engineering workflows, and enhancement of team technical capabilities.
Lead monitoring, observability, incident management, root cause analysis, and continuous service improvement.
Provide subject matter leadership on critical issues, guiding troubleshooting, root cause analysis, and long-term remediation plans.
Advance the Data Hub Agile transformation journey.
Improve backlog health, prioritization, sprint discipline, estimation quality, and delivery predictability.
Establish measurable engineering and flow metrics.
Continuously optimize team productivity, delivery throughput, and value realization.
Improve visibility, governance, prioritization, and capacity management.
Ensure Data Hub solutions meet reliability, performance, compliance, and maintainability requirements.
Lead monitoring, observability, incident management, root cause analysis, and continuous service improvement.