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About The Team
About The Role
Key Responsibilities
- Develop and execute a comprehensive data and AI platform strategy aligned with business objectives and industry best practices.
- Define the roadmap for data platform modernization, incorporating cloud-native technologies, microservices architecture, and data mesh concepts.
- Champion the adoption of new technologies and methodologies to improve platform efficiency, performance, and capabilities.
- Stay abreast of the latest trends and advancements in Data Engineering, AI/ML, and data platform technologies, including DevSecOps, SRE, GenAI and data platform modernization.
- Communicate the vision and strategy for the data and AI platform to stakeholders across the organization, including senior leadership.
Team Management \& Development
- Build, lead, and mentor a high-performing team of engineers, architects, and administrators.
- Foster a culture of collaboration, innovation, continuous learning, and knowledge sharing within the team.
- Conduct performance reviews, provide constructive feedback, and identify development opportunities for team members.
Data Architecture \& Engineering
- Oversee the design, implementation, and maintenance of a robust, secure, and scalable data infrastructure, including integration platforms, to ensure seamless data flow across the organization.
- Architect and implement data solutions, including data warehouses, data lakes, data pipelines, and data APIs, with a focus on scalability and performance.
- Ensure the optimal performance, availability, and scalability of the data platform, collaborating with IT infrastructure teams to leverage cloud services and advanced technologies.
- Implement and maintain data security protocols, collaborating with data governance teams and addressing any gaps.
MLOps \& GenAI
- Lead the implementation and optimization and efficient deployment, monitoring, and maintenance of AI/ML and GenAI platforms in production environments.
- Integrate GenAI platforms to deliver AI-powered insights and advanced analytics capabilities, driving innovation across business operations, particularly in R\&D and Commercial functions.
- Collaborate closely with IT and Data Analytics teams to ensure that platforms effectively support their use cases while adhering to established standards.
Enterprise Reporting \& Analytics
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Manage the modernization and deployment of enterprise reporting platforms that provide real-time business intelligence and data visualization.
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Ensure these platforms are designed to support business stakeholders in monitoring performance, identifying risks, and making data-driven decisions, all supported by robust data engineering practices.
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Drive the adoption of these platforms and provide training and support to business users.
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Integrate security practices throughout the data lifecycle, including infrastructure as code, automated security testing, and vulnerability management.
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Implement SRE principles like monitoring, alerting, incident management, and automation to ensure high availability and reliability of data and AI platforms.
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Define and track key performance indicators (KPIs) for the data platform, including availability, performance, security, and cost.
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Drive continuous improvement initiatives to enhance the efficiency and effectiveness of data operations.
Cross-Functional Collaboration \& Communication
- Partner with business leaders, product managers, and engineering teams across different departments to understand data requirements and provide data-driven insights.
- Effectively communicate technical concepts to both technical and non-technical audiences, including senior management.
- Present data platform updates, roadmaps, and performance metrics to stakeholders and senior leadership.
Vendor Management \& Operational Efficiency
- Manage relationships with external vendors to ensure they meet internal security, compliance, and performance expectations.
- Lead efforts in demand management, resource planning, and functional outsourcing to deliver high-quality data engineering and integration solutions efficiently and within budget.
- Drive continuous improvement in operational processes, ensuring alignment with business priorities while managing technical debt.
About You
- 15+ years of experience in data engineering or data warehousing.
- 8+ years in a senior leadership role managing teams
- 8+ years leading and inspiring large, diverse teams.
- Strong understanding of data architecture principles, data modeling techniques, and data integration patterns.
- Proficiency in data warehousing technologies (e.g., Snowflake, Redshift), data lake solutions (e.g., Databricks, AWS EMR), and ETL/ELT tools (e.g., Fivetran, dbt).
- Hands-on experience with cloud platforms like AWS, Azure, or GCP, and their data services.
- Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
- Strong understanding of AI/ML concepts and experience with MLOps platforms and tools.
- Familiarity with GenAI technologies and their potential applications in business.
- Knowledge of data governance frameworks, data security best practices, and compliance regulations.
- Familiarity with integrating security practices throughout the data lifecycle, including infrastructure as code, automated security testing, and vulnerability management.
- Experience with implementing SRE principles like monitoring, alerting, incident management, and automation to ensure high availability and reliability of data platforms.
- Extensive experience with modernizing legacy data platforms by leveraging cloud-native technologies, microservices architecture, and data mesh concepts.
Other Qualifications
- Strong Communication \& Collaboration Skills, exceptional ability to effectively communicate technical concepts to both technical and non-technical audiences, build consensus, and influence stakeholders at all levels.
- Excellent Problem-Solving \& Analytical Skills, proven ability to identify and solve complex data challenges, analyze data patterns, and drive data-driven decision-making.
- Strategic Thinking \& Business Acumen, ability to align data and AI initiatives with broader business goals and demonstrate the value of data-driven decision-making.
- Open to 50% flex-hybrid reporting to our Toronto office
Workday Pay Transparency Statement
Our Approach to Flexible Work spend at least half (50%) of our time each quarter in the office or in the field
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