Director of Engineering, Data

Role overview

Responsibilities

• Own data platform architecture, including ingestion, transformation, orchestration, and evolving ML and streaming strategies.
• Build scalable customer and partner data onboarding capabilities that deliver data reliably and reduce time to first value.
• Run production-grade data systems with monitoring, alerting, SLAs, incident response, lineage, freshness, and dataset ownership.
• Build data foundations for AI and ML products, including training, inference, and feature infrastructure.
• Establish access controls, data classification, audit trails, PII handling, and practical data contracts.
• Hire and develop the data engineering team and set operating rhythms for planning, on-call work, and stakeholder intake.
• Remain hands-on when needed, including writing code, debugging issues, and carrying operational responsibility.
• Influence cross-functional stakeholders and translate technical decisions into business impact.

Experience and qualifications

• 10+ years in data or software engineering and experience building and scaling teams of 10+ engineers.
• Hands-on experience with modern data platforms such as Snowflake or Databricks, dbt and ELT, orchestration such as Airflow, and streaming such as Kafka or Kinesis.
• Direct experience building production ML data systems, with knowledge of feature stores, training data, real-time inference, and build-versus-buy tradeoffs.
• Track record of data operations, including monitoring, SLAs, incident response, and data quality.
• Experience delivering customer-facing reporting, analytics, or APIs in B2B SaaS environments.
• Strong architectural judgment and practical governance experience covering security, compliance, documentation, contracts, and access models.
• AWS is strongly preferred. Series B-D scale-up experience is a plus; vertical SaaS or construction technology experience is helpful but not required.
• Able to work through ambiguity, deliver an MVP, make long-term technical decisions, and influence stakeholders across functions.

Technical focus

• Modern data platforms, data pipelines, ELT, orchestration, and streaming.
• Production ML systems, feature stores, training data, and real-time inference.
• Data governance, security, compliance, quality, monitoring, and operational reliability.

Compensation

• Base salary range is $176,000 to $235,000, plus an annual bonus and equity.

Workplace and benefits

• The company describes flexible PTO and hybrid work schedules, with hubs including Toronto and lunch provided on in-office days.
• Benefits include an equity grant, work-from-home allowance, company events, and opportunities for growth.