Data Engineer

Canfor•Vancouver, BC

28d•CA$120,000 - CA$140,000•Onsite

About The Position

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. 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.

Requirements

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 engineering, ELT, and streaming solutions, including technical leadership across complex initiatives.
Proven track record designing and evolving scalable, secure, and cost-effective cloud data architectures in an enterprise environment.
Proven experience in database design, data schema design and data modeling
Experience defining and governing enterprise data architecture standards, engineering frameworks, and reference patterns.
Experience delivering ETL/ELT solutions for importing data from a wide variety of sources
Experience working with Microsoft Fabric or Azure Products: Microsoft Fabric, Azure Synapse, Azure Data Factory, Azure Purview, Azure DevOps, etc.
Experience designing and building cloud data architecture to facilitate data pipelines and analysis workflows within a cloud environment (preferably Microsoft Fabric)
Demonstrated experience leading large-scale, cross-functional, or enterprise-wide data platform initiatives from design through implementation.
Demonstrated experience mentoring engineers, leading technical design decisions, and influencing engineering direction without requiring formal people management.
Experience with data observability, monitoring, lineage, testing, and data quality controls in cloud data platforms.
Experience with CI/CD, DevOps practices, and automation for data platform deployment and lifecycle management.
Self-starter with the ability to meet the needs of a demanding business and IT environment
Ability to learn and apply new and emerging concepts and technologies
Willingness to go above and beyond to understand both business and technical aspects
Excellent verbal, listening and written communication skills
Communicates ideas, both technical and solution focused, in an easy-to-understand language
Ability to align technical solutions with business strategy
Excellent analytics, mathematical and creative problem-solving skills
Detail-oriented and self-motivated

Nice To Haves

Experience with Enterprise Resource Planning systems (preferably Oracle) is an asset
Experience enabling data platforms that support advanced analytics, AI/ML, and Generative AI workloads is strongly preferred.

Responsibilities

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.