Director, Data Platform - Data Warehousing & Engineering, Toronto

Director, Data Platform - Data Warehousing & Engineering

The Director of Data Platform provides strategic leadership in scaling Data & AI infrastructure across the enterprise. This role commands the teams responsible for defining, launching, and maintaining technical platform capabilities, including the Data Consumer, Data Producer, and Data Exchange products. Serving as a bridge between complex engineering and business outcomes, the Director ensures data democratization through Natural Language Interaction and decentralized engineering models. This leader must balance operational excellence (FinOps, Observability, Security, Compliance, etc) with the rapid deployment of next-generation analytics and agentic solutions.

Technical Expertise

• Cloud Mastery: Proven ability in scaling Data Platforms on Hyperscalers (GCP preferred, Azure, or AWS).
• Data Architecture: Deep experience with Spark, Databricks, and Data Lakehouse architectures.
• Advanced Analytics: Knowledge of MLOps principles and the infrastructure required to operationalize autonomous data agents.
• Tools & Languages: Proficiency in SQL, Python, or PySpark, and familiarity with visualization tools like PowerBI, Sigma, Looker, etc.
• Platform Expertise: Proficiency in building modern deployment architectures, CI/CD, Infrastructure as Code, and Software Supply Chains.

Platform Strategy & Execution

• Define and build the technical products necessary for secure, enterprise-wide data access, specifically focusing on Data Consumer and Producer interfaces.
• Direct the roadmap for the Self-Serve Data Platform, balancing technical debt with the need for faster time-to-value and superior user experience.
• Drive the transition to a Data Mesh architecture that unifies structured and unstructured data across GCP, Databricks, and next-gen AI and Data platforms
• Operationalize FinOps and cloud consumption governance to maximize ROI and minimize non-value-add compute costs.

AI & Data Enablement

• Close the gap from data to decisions by building ready-to-deploy Analytics and Agentic AI solutions.
• Lead the adoption of Generative AI tools within engineering workflows to automate code generation, documentation, and testing.
• Nurture a consultative team culture through "Forward-deployed Data Engineering," embedding experts into business units to solve data-driven challenges.
• Implement AI/ML-based tools for proactive data quality monitoring and self-correcting anomaly detection.

Governance & Security

• Ensure data security and compliance are "built-in" features of the platform, meeting rigorous financial regulatory standards.
• Maintain the Enterprise Data Catalogs and metadata management systems to foster data discovery across all business lines.
• Establish architectural solutions that enable rapid but secure deployment, leveraging policy as code, IAC Factory patterns

Leadership & People Management

Team Leadership

• Manage and mentor a high-performing team of engineers and product owners, aligning individual growth with strategic organizational goals.
• Lead the shift in engineering roles from manual coding to prompt engineering, architectural design, and data product ownership.
• Forecast resourcing needs and develop alternative hiring plans to address technical talent gaps.

Strategic Collaboration

• Partner with the Managing Director to define resources, organizational structures, and cultural changes needed to support the technology strategy.
• Collaborate with Platform & Architecture and Cybersecurity to ensure all platform guardrails meet corporate compliance requirements.
• Serve as an evangelist for data-driven culture, sharing knowledge through internal workshops and enabling self-service support for the organization

Experience & Education

• Industry Experience: Extensive background in Financial Services or similarly regulated industries.
• Product Mindset: Demonstrated experience in Product Management for technical platforms or client-focused service delivery.
• Transformation Leadership: Proven track record of leading large-scale technical migrations (e.g., legacy warehouses to Cloud).
• Education: Bachelors, Masters, or PhD in Mathematics, Statistics, Computer Science, or a related field is preferred.

Hybrid/Remote:

• This role requires three days of in-office presence per week for Greater Toronto Area (GTA) residents. For candidates residing outside the GTA, a remote workplace arrangement is available.