Job Summary Reporting to the Vice President of Data, the Director of Data Engineering leads and evolves the data engineering function across the organization. This is a senior, hands-on leadership role responsible for setting technical strategy while actively contributing to critical data initiatives. The position oversees the end-to-end design, delivery, and reliability of a cloud-based data platform, from raw data ingestion through the creation of analytics-ready datasets. The Director manages a team of data and platform engineers, defines ELT and orchestration strategies, and partners with business stakeholders to drive a data-driven culture. This job is onsite in Toronto, CA. 4 days in-office is required.

Key Responsibilities Data Engineering Strategy & Roadmap Own and drive the data engineering roadmap, balancing new capabilities with platform reliability, technical debt reduction, and cost optimization Evaluate and recommend tools, cloud services, and architectural patterns; build business cases for platform investments Stay current with modern data ecosystem capabilities and translate them into actionable roadmap initiatives Technical Leadership & Delivery Lead the design and ongoing development of a medallion architecture (bronze, silver, gold layers), including ingestion, transformation, and curated data models Define and enforce ELT standards using dbt, including model layering, testing frameworks, and deployment practices Develop reusable dbt macros, packages, and testing frameworks to improve data quality and engineering efficiency Oversee dbt project governance, including environment management (dev/qa/prod), deployment pipelines, and artifact management Drive adoption of advanced dbt capabilities such as incremental models, snapshots, and semantic layer design Lead orchestration design and operations (e.g., Airflow, Prefect, or Dagster), ensuring pipelines are modular, reliable, and observable Establish standards for orchestration including dependency management, retry logic, SLA monitoring, and backfills Ensure end-to-end pipeline reliability by integrating ingestion, transformation, and downstream consumption layers Enforce engineering best practices including code reviews, branching strategies, and release management Implement and monitor data quality gates at each stage of the data pipeline Partner with domain teams to onboard new data sources with standardized naming, partitioning, and retention strategies Optimize platform performance and manage cloud costs through monitoring, query tuning, and resource management Data Quality, Observability & Reliability Implement data observability frameworks to detect anomalies, schema changes, and pipeline failures Define and track service level objectives (SLOs) for critical data products Lead incident response and post-mortem processes to continuously improve system reliability People Leadership & Team Development Set clear team objectives aligned with data strategy and ensure accountability for delivery and quality Act as the primary escalation point for technical challenges, dependencies, and production issues Lead hiring, mentoring, coaching, and performance management of the data engineering team Oversee resource planning, headcount management, and team development to ensure operational continuity Qualifications Education & Experience Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent experience) 10+ years of hands-on data engineering experience across modern data platforms and tools 5+ years of experience leading and developing data engineering teams Proven success delivering scalable ELT/data integration solutions in production environments Technical Skills & Expertise Strong SQL expertise and deep understanding of data modeling methodologies (e.g., Kimball, Inmon, Data Vault) Hands-on experience with modern cloud data platforms such as Snowflake and/or Databricks Experience with data pipeline development using dbt, including models, testing, and modular design Expertise with orchestration frameworks such as Airflow, Prefect, or Dagster Experience designing data architectures using medallion/lakehouse patterns Familiarity with cloud ecosystems (AWS, Azure, or GCP) and native data services Experience with CI/CD pipelines for data workflows (e.g., GitHub Actions, dbt Cloud) Knowledge of data quality and observability tools (e.g., Monte Carlo, Great Expectations) Leadership Skills Strong people management and team leadership capabilities Ability to influence technical direction and align cross-functional stakeholders Strategic thinker with a hands-on, execution-focused approach