Principal DataOps Engineer (DevOps for Data)

Workday•Pleasanton, CA

5d•Hybrid

About The Position

The Principal DataOps Engineer (P5) serves as a senior technical authority, strategic leader, and architect for Workday’s enterprise data platform program. The role defines long-term vision, execution standards, and multi-quarter architecture roadmaps for mission-critical cloud infrastructure, resolving complex technical ambiguity and leading multi-team transformations to deliver data-as-a-product assets at scale. Within Workday’s Enterprise Data and Analytics team, the position governs DataOps, Data Product, and Active Data Governance frameworks across the enterprise. It develops operational blueprints for integrating AI capabilities into continuous delivery, building language-model solutions and automated workflows, and creating agent networks in Sana and Gemini to automate routine work and increase delivery velocity. The role is open to a 50% hybrid reporting arrangement at the Pleasanton campus. Workday’s flexible-work approach calls for spending at least half of each quarter in an office or in the field, while allowing schedules to accommodate business, team, and personal needs. Compensation for the primary Pleasanton location is $175,400–$263,200 annually; additional U.S. locations have a base-pay range of $147,300–$263,200. The role may be eligible for a bonus plan or role-specific commission/bonus and annual refresh stock grants.

Requirements

12+ years of professional technical leadership experience in DataOps, DevOps, cloud automation, program management, or platform engineering within enterprise data and analytics.
12+ years of experience managing and scaling interrelated data platform products, cluster configurations, and CI/CD networks at enterprise scale.
12+ years of experience demonstrating leadership presence and influencing VP- and SVP-level stakeholders to build technical consensus and drive cross-functional organizational change.
Expert-level technical command of high-scale distributed data platforms, capital asset cluster budgeting, modern warehouse layouts, Terraform Infrastructure as Code, and hyperscale pipeline orchestration boundaries.
Master-level capability directing AI-forward validation and deployment frameworks, setting branch benchmark policies for autonomous code generation, and designing agent networks.
Comprehensive expertise in federated active metadata layers, predictive data-trust telemetry, compliance logic, and complex asset lifecycle management.
Superior ability to navigate high-pressure corporate environments and complex technical landscapes, secure consensus across business units, and serve as a change agent and thought leader for DevOps and engineering communities.

Nice To Haves

Hands-on experience or familiarity with GitHub Actions, Snowflake, Fivetran, Sigma, and Atlan, or similar enterprise data platforms.

Responsibilities

Define long-term vision, execution standards, and multi-quarter architecture blueprints for a mission-critical enterprise cloud infrastructure portfolio.
Govern global platform administration paradigms, Terraform Infrastructure as Code architecture standards, and CI/CD pipelines; mentor senior engineers and establish foundational infrastructure modules.
Establish and govern knowledge synchronization standards and Documentation as Code practices across global DevOps and Analytics Engineering job families.
Mitigate enterprise infrastructure and architecture risks; design high-scale zero-trust data reliability parameters and predictive platform telemetry to support data platform uptime during periods of growth.
Sponsor and chair cross-functional technical review boards, align technical direction across business segments, lead multi-team infrastructure transformations, navigate platform migration blockers, and manage capital resource planning.
Establish operational blueprints for embedding DataOps and Active Data Governance principles into master software continuous delivery pipelines.
Build division-level AI agent networks and automated scripts in Cursor IDE, Sana, and Gemini to automate code refactoring, system validation, and portfolio tracking at enterprise scale.
Direct AI-forward validation and deployment frameworks, set branch benchmark policies for autonomous code-generation loops, and design agent networks to reduce manual infrastructure workflows.
Use federated active metadata layers and predictive data-trust telemetry to embed compliance logic into global platform environments and manage complex asset lifecycles.