Engineering Manager, Data Modeling

Accountabilities:

• • 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 AI tools to improve engineering productivity and data-modeling workflows.
  • Experience supporting datasets used for AI/ML model training is a plus.
  • Previous experience within a data infrastructure or data platform team is an advantage.
  • Ability to work effectively in a remote environment with a high degree of autonomy, ownership, and collaboration.

  Benefits:

  • Fully remote position based in Canada.
  • Competitive compensation determined according to location, level, relevant knowledge, skills, and professional experience.
  • Opportunity to lead a high-leverage data modeling function with significant influence across the organization.
  • Hands-on exposure to modern data engineering technologies, including SQL, Python, Spark, Databricks, Airflow, and Astronomer.
  • Opportunity to shape shared data standards, semantic definitions, architecture, and long-term technical direction.
  • Cross-functional collaboration with Product, Engineering, Data Platform, Analytics, Data Science, and ML teams.
  • Meaningful opportunity to build durable data foundations supporting analytics, customer-facing insights, and AI/ML initiatives.
  • Leadership and mentoring opportunities within a technically focused, collaborative engineering environment.
  • Reasonable accommodations are available for individuals with disabilities throughout the application, interview, and employment process.