Director, Data Engineering (AI Native)

Jobgether · Canada

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Director, Data Engineering (AI Native) based in Canada.

This is a senior leadership opportunity to shape and scale a modern data engineering organization within a high-growth, remote-first technology environment. The role owns end-to-end data systems, from ingestion and transformation to analytics and consumption by both humans and AI/LLM systems. You will lead multiple engineering teams through managers, defining the technical vision for data platforms, analytics engineering, and data governance. The position plays a critical role in enabling product, marketing, finance, and analytics teams with trusted, scalable, and self-serve data. It requires deep technical credibility combined with strong business acumen to influence decisions at the executive level. You will also drive AI-native practices across the data stack to improve speed, quality, and scalability of insights. This is a highly strategic role where leadership, architecture, and cross-functional impact converge.

Accountabilities

  • Define and own the end-to-end technical strategy for data engineering, including ingestion, transformation, modeling, orchestration, and serving layers across the organization.
  • Lead and scale multiple engineering teams through managers, ensuring strong execution, talent development, and alignment with organizational priorities.
  • Oversee analytics engineering practices, including data modeling standards, dbt architecture, testing frameworks, CI/CD workflows, and semantic layer design.
  • Manage and optimize modern data platforms such as Databricks or equivalent lakehouse ecosystems, ensuring reliability, performance, and cost efficiency at scale.
  • Partner with cross-functional leaders in Product, Engineering, Finance, and Marketing to translate business needs into scalable data solutions and roadmaps.
  • Establish strong data governance, quality, documentation, and observability standards to enable trusted self-serve analytics across the organization.
  • Drive AI-native transformation of the data function by integrating AI tools into workflows, pipeline optimization, and data quality processes.
  • Own workforce planning, vendor strategy, and budget allocation for data infrastructure and engineering investments.
  • Requirements

    • 8–10+ years of experience in data engineering, analytics engineering, or data platform roles within high-scale technology environments.
    • 5+ years of people management experience, including at least 3+ years managing managers and leading multi-team organizations.
    • Strong technical expertise in modern data stacks, including dbt, Databricks, Snowflake, BigQuery, or equivalent platforms.
    • Deep understanding of ELT/ETL architecture, data modeling (dimensional and semantic layers), and large-scale data pipeline design.
    • Proven ability to lead architectural decisions and evaluate tradeoffs across data systems, performance, scalability, and cost.
    • Strong executive communication skills with the ability to translate complex technical concepts into clear business insights.
    • Demonstrated experience influencing cross-functional stakeholders and driving alignment across competing priorities.
    • AI-native mindset with hands-on experience using AI tools to improve engineering productivity and data workflows.
    • Strong business acumen with understanding of product analytics, growth metrics, and monetization models.
    • Experience managing distributed or remote-first engineering teams is highly valued.
    • Benefits

      • Competitive compensation package including base salary and equity
      • Comprehensive medical, dental, vision, life, and disability coverage
      • Retirement savings plans with employer contributions (RRSP/401k equivalent depending on location)
      • Flexible remote-first work environment across Canada
      • Generous PTO and company-wide paid time off days
      • Learning and development budget for training, courses, and career growth
      • Home office and equipment support for remote productivity
      • Equity program enabling long-term participation in company success
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