Principal Analytics Engineer

Workday•Pleasanton, CA

2d•Hybrid

About The Position

Workday has launched an Enterprise Data and Analytics (ED&A) Team to Transform and optimize the way Workday creates and shares trusted data to drive actionable insights and data led innovation across the enterprise. To enable this strategy, the ED&A team has worked with the business to identify critical business areas in which data and analytics can make a material difference in the execution of Workday’s strategic goals. Each of these goals is being organized as a data product with a dedicated multi-functional team to drive a diverse set of data management, governance, and data analytics to realize relevant, measurable business change. As part of ED&A and enabling this strategy, the Analytics Engineering team is at the forefront of transforming data into reliable and insightful assets. We are responsible for building scalable data products, ensuring data quality and governance through version control, and creating best practices for the organization. The team also operates at the intersection of Product, Engineering, and Business Operation, owning the transformation layer of our modern data stack (Snowflake, dbt). We are seeking a Principal Analytics Engineer with skills in data product development, pipeline management, data modeling, data transformation, and data analytics to support enterprise analytics and AI initiatives. The Principal Analytics Engineer's role moves beyond execution to focus on architectural governance, acting as the dbt expert to define and enforce data product development best practices for scalability and cost-efficiency. This role requires a passion for coaching junior engineers, providing deep technical mentorship to foster a culture of engineering rigor and elevate team performance. You will need to master the complex organizational dynamics inherent in cross-functional projects, acting as the key technical liaison to build consensus and drive the adoption of unified enterprise metrics. Ultimately, you will be responsible for defining the strategy for foundational data projects, ensuring all business teams have access to optimized, efficient and trusted data products.

Requirements

7+ years of professional experience in an Analytics Engineering or Data Engineering role, preferably within a SaaS or high-tech environment.
7+ years of professional experience in SQL and strong production experience with a major cloud data warehouse (Snowflake, BigQuery, Redshift).
Extensive experience with DBT, including advanced features (macros, packages, source freshness, custom tests).
Strong familiarity with version control (GitHub), CI/CD, and modern development workflows.
Strong understanding of data warehousing concepts and dimensional modeling.

Nice To Haves

Experience in a technical coaching or mentoring role with demonstrable impact on junior engineers' development.
Demonstrated ability to manage requirements and expectations across multiple, competing business units.
Strong communicator who can build trusted partnerships across GTM, Finance, and Exec stakeholders.
Experience with a major orchestration tool and defining complex data dependencies.
Deep functional knowledge of core SaaS business domains (e.g., Salesforce/CRM data, Product telemetry, Financial modeling).
Proficiency in Python for scripting, data manipulation, and pipeline orchestration.
Bias for action - you prefer launching usable, iterative data models that deliver immediate value over waiting for perfect solutions.
Comfortable working through ambiguity in fast-moving, cross-functional environments.
Familiarity with data governance tools, data catalogs, and data observability solutions.
Bachelor’s degree in Computer Science, Engineering, or quantitative field.
Master's or Ph.D. degree preferred.

Responsibilities

Architect, design, and lead the build-out of end-to-end performant, reliable, and scalable data pipelines and the transformation layer.
Act as the dbt subject matter expert, defining and championing data modeling standards and best practices across the organization while managing the full lifecycle of complex dimensional models and metrics from prototyping to production.
Partner cross-functionally with Product Owners, Data Analysts, and business leaders (Sales, Marketing, Finance) to scope and deliver high-impact analytics initiatives, ensuring analytics requirements are clearly understood and effectively implemented.
Operate as a highly independent individual contributor, solving complex, ambiguous problems and delivering high-quality, architecturally sound solutions with minimal oversight and a high degree of ownership over critical data domains.
Mentor, guide, and coach junior and mid-level engineers to deliver complex and next-generation features, actively instilling a culture of software engineering rigor, code quality, best practice, standards, and technical excellence within the team.
Master the dynamics of high-stakes projects, expertly navigating stakeholder and internal complexities to align business needs with technical feasibility and secure consensus on enterprise-wide metric definitions.
Design and build database architectures to handle massive and complex data volumes, skillfully balancing computational load, query latency, and data warehouse cost efficiency, integrating strong data quality audits and testing frameworks at scale to ensure resilience.
Boost overall data team productivity by proactively identifying technical debt, improving tooling, automating complex workflows, and streamlining processes for transformation and deployment.
Bring a customer-centric, product-oriented mindset to the table, collaborating with external and internal stakeholders to resolve ambiguities and ensure shipped data features are impactful, reliable, and align with business outcomes.
Build and maintain user friendly documentation for data models, data processes, workflows, and systems for transparency and knowledge sharing.