VP, Data, Automation, & AI

at  Canada Goose Inc

Company Description
Canada Goose isn’t like anything else. We’ve built something great, something special - an iconic lifestyle brand with an inspirational and authentic story. At the heart of it is our promise to inspire and enable all people to thrive in the world outside. To Live in the Open. At Canada Goose, you’re part of a movement that belongs to something bigger. One that seeks out the restorative power of nature and is driven by a purpose to keep the planet cold and the people on it warm. We endure any condition, observe every detail, and are building a community that believes in living bravely and coming together to support game-changing people.
Here, opportunities are everywhere - to try something new, to learn, to do meaningful and impactful work, and they’re yours for the taking.
Job Description
Our VP, Data, AI and Automation collaborates closely with our Agile PODs to develop cutting-edge AI solutions that drive innovation within the organization. They have a deep understanding of AI strategies, model development, data architecture, and data analysis, coupled with a foundational knowledge of data science methodologies and workflows. The VP of Data, Automation and AI also leads the definition of a scalable data architecture and workflows. They are a dedicated problem-solver and Agile practitioner, thrives in a collaborative environment, working alongside cross-functional Analytics Teams to tackle complex data and automation challenges and deliver actionable insights that drive the overall technology strategy and innovation forward. Your primary responsibility is to develop a cohesive data architecture that supports the organization’s long-term vision. You design efficient data storage structures, data models, standards, and optimize data retrieval for reporting, analytics, and AI purposes.

EXPERIENCE, EDUCATION AND DESIGNATIONS:

• Minimum of 12 years with progressive experience in a senior data management role, Data Architecture and Data Engineering experience, including team leadership
• Bachelor’s or Master’s degree in business, Computer Science, Data Science or a related field.

KNOWLEDGE, SKILLS AND ATTRIBUTES:

• Ability to influence stakeholders across business functions for enterprise adoption and change management.
• Strategic thinker with a problem-solving mindset.
• Strong understanding of data risk, data governance and data as a product.
• Experience leading technical teams at the intersection of governance, automation, and model development, preferably in a global setting.
• Experience with data privacy laws and regulations, and the ability to operationalize from policy to process and procedures.
• Knowledge deidentified / test data creation and management, with a focus on data security and privacy.
• Proven expertise in designing data platforms for large-scale data and diverse data architectures, including warehouses, lakehouses, and integrated data stores
• Experience with data security (including PHI and PII), as well as data privacy regulations (CCPA and GDPR)
• Proficient in addressing data-related challenges through analytical problem-solving and aligning data architecture with organizational business goals and objectives
• Exposure to analytics techniques using ML and AI to assist data scientists and analysts in deriving insights from data
• Analytical and problem-solving skills to address data-related challenges and find optimal solutions
• Ability to manage projects effectively, plan tasks, set priorities, and meet deadlines in a fast-paced environment
• Proven experience as a Data Architect or Data Engineer with hands-on experience in designing and implementing data solutions on Azure Data Lake and AWS.
• Strong understanding of cloud services and data architecture principles.
• Proficiency in data integration and ETL concepts and practical experience with cloud technologies.
• Knowledge of data storage and processing technologies in Azure, including Azure Data Lake Storage, Azure Data Lake Analytics, and Azure Synapse Analytics and legacy SQL Data Warehouse.
• Experience with data transformation and data preparation tools and techniques.
• Knowledge of data security and compliance practices in the Azure cloud environment.
• Familiarity with data cataloging and metadata management tools and practices.
• Azure certifications related to data and analytics (e.g., Microsoft Certified: Azure Data Engineer Associate) are a plus.
• Strong experience in data architecture and design, knowledge and expertise with GenAI related technologies: Foundation models/GPT, LLM/SLM, vector databases, AI Search/vector search, Langsmith, prompt engineering
• Expertise with modern data architecture and platforms, experience with a variety of database technologies, including relational databases (e.g., SQL Server, Oracle, MySQL), NoSQL databases (e.g., MongoDB, CosmosDB, Cassandra), and cloud-based database services (e.g., Azure SQL Database, Databricks, BigQuery, Snowflake, Amazon RDS)
• Manage multiple data integration/ETL patterns: batch, streaming, API/microservices
• Understanding of implementation of architecture using Azure data technology stack
• Hands-on experience with data integration tools (e.g., ADF, Synapse, DataBricks) and ETL development
• Proficiency in designing conceptual, logical, and physical data models to represent organizational data assets and facilitate efficient data storage, retrieval, and analysis
• Knowledge of data governance frameworks, policies, and best practices to ensure data quality, integrity, security, and compliance with regulatory requirements
• Excellent leadership and communication skills, with the ability to collaborate across various business functions.
• Excellent leadership, communication, and project management skills.
• Experience with modern cloud and reporting tooling (AWS, GCP, Azure, PowerBI, Looker, Domo, etc.)
  Qualifications

• Collaborate with internal departments to define the strategic data architecture vision, roadmap, and improve processes, data detail, and business applications.
• Establish guidelines, controls, and processes to make data available for developing scalable data-driven solutions for Analytics and AI.
• Enforce data ingestion, integration, and access patterns to support both real-time and batch-based consumer data needs.
• Implement security measures to safeguard sensitive data from unauthorized access, ensuring data privacy, and compliance with relevant regulations.
• Plan and oversee data migration processes when transitioning to new data systems and platforms.
• Drive continuous data transformation to minimize technical debt.
• Display strong thought leadership in pursuit of modern data architecture principles and technology modernization.
• Define and lead technology proof of concepts to ensure feasibility of new data technology solutions.
• Create comprehensive documentation for design, and processes to support ongoing maintenance and knowledge sharing.
• Conduct architectural reviews to ensure the solution addresses customer pain points, business, and technical requirements, and alignment to standards and best practices.
• Prepare and deliver comprehensive communications to convey data architectural direction and how it aligns with Enterprise Strategy and able to sell the strategy and implementation to Executive Leadership.
• Establish and maintain strong data management capabilities including developing and maintaining accurate data lineage for critical data, documentation of E2E processes, establishment of SLAs with appropriate data providers, establishment of data quality measurements and reporting, establishment of issue management process and procedures.
• Implement Data governance.
• Work in conjunction with data consumers and domain stakeholders to support data quality rules, standards, and thresholds to ensure appropriate data quality monitoring practices are in place.
• Ensure compliance with industry regulations and internal policies related to data management.
• Actively track regulatory changes and anticipating the impact these will have on the Domain’s data governance and reporting.
• Set Technical Standards and Best Practices: Research and recommend technical standards, ensuring the architecture aligns with overall technology and product strategy. Be hands-on in implementing core components reusable across applications.
• Stakeholder Collaboration: Collaborate closely with external and internal stakeholders, including Business Teams and Product Managers. Define roadmaps, understand functional requirements, and lead the team through the end-to-end development process.