Director, AI Engineering Operations

About the Role Workday is seeking a Director, AI Engineering Operations & Data Engineering who will be a critical leader within the Enterprise Data & AI Technologies and Architecture (EDATA) organization at Workday. This role oversees the strategic direction and execution of both our core Data Engineering & Integrations function and a newly formed AI Engineering Operations function. You'll ensure the scalable, reliable, and efficient flow of data and the seamless deployment and operation of our AI models, serving as a key partner to our internal business stakeholders.This role requires a leader with deep expertise in modern data architectures/frameworks, cloud-native platforms (AWS, Snowflake, Databricks, etc.), and the emerging field of MLOps/AIOps. You must be adept at building and managing high-performing engineering teams, driving complex technical roadmaps, and building both technical and business relationships across the organization.Job Duties: Leadership & StrategyDefine and champion the vision, strategy, and roadmap for Data Engineering, Integrations, and AI Engineering Operations.Lead, mentor, and grow a diverse team of data engineers, integration specialists, and AI/MLOps engineers, fostering a culture of innovation, reliability, and ownership.Partner with the VP of EDATA and other EDATA Directors (Data Platforms, Data SRE, AI Strategy, etc.) to ensure a cohesive and well-governed enterprise data and AI ecosystem.Data Engineering & IntegrationsOversee the design, development, and maintenance of robust, scalable, and high-performance ETL/ELT data pipelines utilizing platforms like Snowflake and Databricks.Ensure data quality, integrity, and security standards are strictly enforced within all data pipelines and integrations.Manage the strategy and execution of all enterprise data integrations, connecting core business systems (e.g., Sales, Finance, HR) to the central data platforms.AI Engineering Operations (AIOps/MLOps)Establish and lead the new AI Engineering Operations function, defining its processes, best practices, and technology stack.Implement and manage the MLOps lifecycle, including model training orchestration, continuous integration/continuous deployment (CI/CD) for models, and automated testing.Design and provision the production environment for AI models, ensuring scalability, low-latency inference, and seamless integration with end-user applications.Collaborate with Data Platforms, Data Science & Innovation and AI Architecture to industrialize experimental models into reliable, production-ready services.Operational ExcellenceWork closely with Data SRE (Change Management, QA, Monitoring) to implement best practices for pipeline and model observability, alerting, and incident response.Ensure the team adheres to architectural standards (defined by the Data & Analytics Architecture team) and security policies (defined by the Data & Analytics Security Architecture team).Manage project portfolios, resource allocation, and budget for both Data Engineering and AI Engineering Operations.About YouBasic Qualifications: 10+ years of experience in data engineering, software engineering, or a related technical field.10+ years of expertise with major cloud platforms (AWS, Snowflake, Databricks) and their ecosystems.7+ years of experience managing and leading high-performing engineering teams, including managers.Proven experience in designing and scaling complex, enterprise-level ETL/ELT pipelines.Experience building and leading an MLOps/AI Engineering Operations function. Deep familiarity with MLOps tools and methodologies (e.g., MLflow, Kubeflow, Sagemaker/Azure ML/GCP Vertex AI equivalents).Strong understanding of Data and AI Architecture principles and a commitment to secure, governed data practices.Proven track record of developing and implementing data engineering and AI Engineering Ops strategies and frameworks in a corporate environment.Other Qualifications: Excellent communication and presentation skills, with the ability to articulate complex technical concepts to non-technical stakeholders.Leadership and team management experience, with the ability to inspire and develop talent.Experience working with cross-functional teams and managing complex projects.Deep understanding of AI technologies, machine learning models, and data analytics.Strong strategic thinking and problem-solving skills.Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Business Administration, or a related field. PhD is a plus.Workday Pay Transparency Statement The annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday’s comprehensive benefits, please .Primary Location: CAN.ON.TorontoPrimary CAN Base Pay Range: $151,800 - $227,800 CADAdditional CAN Location(s) Base Pay Range: $151,800 - $227,800 CAD