The Enterprise Analytics function at Canfor has a vision to deliver data, information and tools to employees at all operating levels and across all business lines to support decision-making that drives operational efficiency, product diversification and innovation.
Why This Role Matters
- Help shape a modern data and analytics foundation that powers real-time insights, AI-driven decisions, and enterprise-wide impact.
- Be at the forefront of advancing our data and AI strategy, influencing how teams across the business work, decide, and innovate.
The Lead Data Engineer provides technical leadership for the design, development, and evolution of enterprise data architecture and data engineering practices. This is a hands-on lead role that combines technical delivery with architecture leadership, guiding solution design, engineering standards, and implementation across the data platform. The role establishes engineering standards, guides implementation across multiple data initiatives, and is accountable for delivering scalable, secure, and high-performing data solutions that align with business objectives and support innovation across cloud-based analytics platforms.
The role requires strong partnership with business, analytics, data science, architecture, and IT teams to shape Canfor’s next-generation analytics platform, lead delivery across multiple workstreams, and ensure data platform capabilities effectively support enterprise reporting, advanced analytics, AI, and operational decision-making.
A day in the life of the Lead, Data Engineer includes:
- Lead the design and implementation of batch, real-time, and streaming data pipelines in Microsoft Fabric, while establishing engineering standards, reusable patterns, and delivery best practices.
- Design and build event-driven and near real-time data processing solutions that enable reliable, scalable data movement
- Own the evolution of the data platform, including scalability, reliability, observability, resiliency, and cost optimization.
- Enable data platforms that support AI/ML and Generative AI use cases
- Establish data observability, monitoring, and data quality SLAs to ensure trusted, resilient data solutions across the analytics platform
- Establish and enforce processes and controls that maintain the security, quality, accountability, availability, and compliance of enterprise data assets.
- Partner with business and IT leaders to shape roadmaps, lead delivery planning, manage technical tradeoffs, and execute projects within time and budget constraints.
- Establish and maintain architecture, engineering, operational, and support documentation standards for the data platform.
- Translate business requirements into scalable data platform designs and guide solution implementation to support analytics and reporting delivery.
- Partner with analytics and data science teams to operationalize advanced analytics solutions
- Lead architecture and design decisions for enterprise data solutions, while defining and governing data engineering standards, reference patterns, and best practices.
- Evaluate, recommend, and guide the adoption of new tools, frameworks, and technologies aligned with platform strategy, engineering maturity, and business value.
- Lead design reviews, code reviews, and technical coaching for data engineering team members to ensure quality, consistency, and capability growth across the team.
- Define and enforce data engineering standards, CI/CD practices, testing approaches, and release management processes for the enterprise data platform.
- Lead technical planning across multiple workstreams, proactively identifying risks, dependencies, and design tradeoffs.
- Champion data platform capabilities that support AI/ML, Generative AI, semantic models, and enterprise data product use cases.
For this role, you’ll come equipped with:
- Bachelor's degree in Computer Science, Information Systems, Mathematics, or Statistics
- 7–10+ years of experience in enterprise data warehousing, analytics, and data platform engineering.
- 7+ years of demonstrated experience designing and delivering data…
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