Principal Engineer, Data and AI Platforms

Your work days are brighter here.

At Workday, it all began with a conversation over breakfast. When our founders met at a sunny California diner, they came up with an idea to revolutionize the enterprise software market. And when we began to rise, one thing that really set us apart was our culture. A culture which was driven by our value of putting our people first. And ever since, the happiness, development, and contribution of every Workmate is central to who we are. Our Workmates believe a healthy employee-centric, collaborative culture is the essential mix of ingredients for success in business. That’s why we look after our people, communities and the planet while still being profitable. Feel encouraged to shine, however that manifests: you don’t need to hide who you are. You can feel the energy and the passion, it's what makes us unique. Inspired to make a brighter work day for all and transform with us to the next stage of our growth journey? Bring your brightest version of you and have a brighter work day here.

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About the Team

The Enterprise Data Services (EDS) organization is a multifaceted and evolving team that is spearheading Workday’s growth through trusted data excellence, innovation, and architectural thought leadership. Equipped with an array of skills in data science, engineering, and analytics, this team orchestrates the flow of data across our growing company while ensuring data accessibility, accuracy, and security. With a steadfast focus on innovation and efficiency, Workmates in EDS enable the transformation of sophisticated data sets into actionable insights that fuel strategic decisions and position Workday at the forefront of the technology industry.

About the Role

We are seeking a highly skilled and experienced Principal Engineer, Data and AI Platforms to lead our engineering efforts and drive the evolution of our Data and AI platform. The ideal candidate will possess a strong background in designing, building, and managing large-scale Data and AI systems, with a focus on automation, scalability, and reliability.

Data Leadership

• Define and champion the vision for a modern, scalable, and secure data platform, incorporating cloud-native technologies and drive adoption of best practices in data modeling, data warehousing, and data lake architecture.

• Champion effective Data Platform principles and practices, ensuring automated data pipelines, CI/CD, and robust monitoring for efficient operations. Optimize data ingestion, transformation, and serving layers..

• Design and implement scalable Data and ML/AI platforms to accelerate the development, deployment, and management of ML/AI solutions in production. Ensure the data infrastructure supports the full ML/AI lifecycle.

• Optimize data infrastructure, applying concepts such as FinOps, Infrastructure as a Code (IaC) for data and ML/AI infrastructure, promoting automation and CI/CD. Lead the governance of data architecture.

• Lead cross-functional collaboration between data scientists, platform engineers, data engineers, operations, security and business teams to ensure alignment and effective data delivery.

• Keep abreast of the latest data and AI technologies, proactively identifying and evaluating new tools and methodologies to improve efficiency and performance of the data platform.

Robust Development Lifecycle for Data Platforms

• Partner with engineering and product development teams to understand their needs and translate requirements into the relevant data and ML/AI workflows..

• Lead the selection, integration, and optimization of DevOps tools and technologies specific to data and ML/AI, including:

• CI/CD platforms (e.g., Jenkins, GitLab CI/CD) with integrations for data and ML/AI workflows.

• Infrastructure as Code (IaC) tools (e.g., Terraform) for provisioning and handling data and ML/AI infrastructure.

• Containerization technologies (e.g., Docker, Kubernetes) for deploying and scaling data and ML/AI applications.

Architectural Standards & Best Practices

• Establish and enforce architectural standards, guidelines, best practices, and frameworks specific to data workloads, ensuring consistency and quality across all projects and systems.

• Promote a culture of automation, continuous integration, continuous delivery, and robust path to production across our data platforms. Drive the implementation of DataOps and MLOps methodologies.

Collaboration & Communication

• Foster a culture of collaboration and shared responsibility between data scientists, platform engineers, data engineers, operations, and security teams, promoting effective communication and knowledge sharing.

• Effectively communicate technical concepts and solutions to both technical and non-technical audiences, including business stakeholders and senior management.

Innovation & Continuous Improvement

• Stay up-to-date with the latest DevOps trends and technologies relevant to data and ML/AI, proactively proposing, evaluating and designing proof-of-concepts on new tools and methodologies to improve efficiency and performance.

• Drive continuous improvement initiatives to optimize DataOps and MLOps processes, enhance automation, and reduce technical debt within the data platforms.

• Mentor and coach other engineers on architectural and engineering best practices specific to data and AI workloads.

About You

Basic Qualifications

• 8+ years of hands-on experience in Data Engineering, DataOps, MLOps, cloud architecture, DevOps, or related fields, with a consistent record of delivering large-scale enterprise data and ML/AI solutions.

• 5+ years of experience with CI/CD pipelines, IaC tools, configuration management tools, containerization technologies, and infrastructure automation, with a focus on data and ML/AI workloads.

• 5+ years of experience with at least one major cloud platform (e.g., AWS, Azure, GCP) and its suite of products, including data and ML/AI services.

• 4+ years of experience with data platforms like Snowflake, Databricks, or similar technologies.

• Bachelor’s degree in Computer Science, Engineering, Business, or a related field.

Other Qualifications:

• Proven ability to establish standard processes and lead multi-functional teams, particularly within data and AI domains.

• Strong understanding of data management principles, data governance, and data security standard methodologies.

• Strong understanding of software development lifecycles and project delivery methodologies (Agile, Waterfall, etc.).

• Experience working collaboratively with third-party vendors, as well as data scientists, ML engineers, software engineers, product development, and cloud operations teams.

• Excellent communication, interpersonal, and leadership skills with the ability to influence and mentor others.

• Strong problem-solving and troubleshooting skills in technical environments.

• Ability to work in a fast-paced, multi-functional team environment.

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 click here.

Primary Location: CAN.ON.Toronto

Primary CAN Base Pay Range: $141,100 - $211,700 CAD

Additional CAN Location(s) Base Pay Range: $141,100 - $211,700 CAD

Our Approach to Flexible Work
 

With Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.

Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.

Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.

Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!