Engineering Manager, Data Modeling

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Engineering Manager, Data Modeling based in Canada. This is a hands-on engineering leadership role focused on building the foundational data layer that powers decision-making across a growing SaaS environment. You’ll lead a small, high-leverage team responsible for core data models, shared metrics, and reliable semantic definitions used across the organization. Your work will support business analytics, customer-facing insights, and AI/ML initiatives while ensuring data remains accurate, consistent, and readily available. You’ll balance technical leadership with hands-on development using modern data platforms and engineering practices. The role offers broad cross-functional exposure, partnering with Product, Engineering, Data Platform, Analytics, and Data Science teams. It’s an opportunity to establish durable data foundations that can be reused across many products and business use cases. Lead the delivery and evolution of foundational data models covering product usage, customers, accounts, and critical business metrics, ensuring they remain reliable, scalable, and broadly reusable. Manage and mentor data professionals while remaining hands-on with technical design, implementation, and problem-solving. Build and operate data products using SQL, Python, and Spark within a modern lakehouse environment such as Databricks, with close attention to quality, performance, availability, and cost. Establish and maintain shared metric definitions and semantic layers so teams across the organization interpret business and product data consistently. Partner with Product and Engineering teams to define foundational product concepts and ensure their representation in shared data models accurately reflects business needs. Collaborate with Data Platform teams on architecture, reliability, orchestration, governance, and long-term maintainability. Serve Business Analytics, Data Science, ML, and customer-facing insights teams by providing trusted, reusable data foundations rather than developing isolated, stakeholder-specific solutions. Set technical direction around modeling standards, architecture, orchestration, and tooling, using technologies such as Airflow or Astronomer where appropriate. Balance immediate delivery needs with investments in durable architecture, data quality, governance, security, and compliance. Encourage the effective use of AI-assisted development tools to improve the way the team designs, builds, tests, and maintains data models. Requirements: 2+ years of experience formally managing or leading data professionals, with demonstrated ability to mentor, guide, and set technical direction. 6+ years of experience building and owning shared, reusable data models within modern data platforms. Strong understanding of SaaS data environments, including product usage, customer, account, and business data. Advanced SQL skills and strong Python proficiency, with hands-on experience using Spark and modern lakehouse technologies such as Databricks. Experience designing data systems around quality, reliability, availability, performance, and cost, including workflow orchestration with Airflow or Astronomer. A reuse-first mindset, with a proven ability to create foundational models and shared metric definitions that serve multiple teams and use cases. Strong systems awareness, including familiarity with governance, security, compliance, scalability, and operational considerations for shared data platforms. Excellent cross-functional communication skills and the ability to collaborate effectively with Product, Engineering, Data Platform, Business Analytics, and Data Science stakeholders. Pragmatic delivery judgment, knowing when to move quickly and when additional investment in durability and reliability is warranted. Demonstrated curiosity about AI-assisted development and an interest in applying emerging