Posted: 19h ago
The role
Requirements
8+ years of professional software engineering experience, with a proven track record of delivering production‑grade systems
4+ years of hands‑on experience designing, building, and operating large‑scale distributed systems across public or private clouds
Deep foundational knowledge of distributed computing principles, including consistency models, concurrency, high availability, and fault tolerance
Bachelor’s degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience
(Desirable) Data Compute & Orchestration: Strong experience working with large‑scale data processing frameworks (specifically Apache Spark) and container‑native infrastructure or resource schedulers (e.g., Kubernetes, YARN)
(Desirable) Algorithmic & System Design: Demonstrated ability to apply advanced algorithmic thinking and data structures to solve complex scalability, scheduling, and resource management problems
(Desirable) API & Architecture: Solid experience designing and implementing robust, scalable APIs (e.g., REST, gRPC) and microservices that support multi‑tenant architectures
(Desirable) Software Craftsmanship: Deep understanding of Object‑Oriented or Functional design principles, with proficiency in languages commonly used in data infrastructure (e.g., Java, Scala, Go, or C++)
(Desirable) Resiliency & Observability: Practical knowledge of building highly available systems, including implementing load balancing, redundancy, observability, and cost‑efficient scaling strategies
(Desirable) Quality & Testing: Strong testing discipline, including unit, integration, and contract testing within a distributed environment to ensure system reliability under heavy workloads
(Desirable) Security Best Practices: Solid understanding of system security principles relevant to cloud and distributed environments, including authentication, authorization, and secure data access
(Desirable) Collaboration & Leadership: Proven ability to own problems end‑to‑end, collaborate cross‑functionally (with SRE, Product, and other engineering teams), and mentor peers
(Desirable) Technical Communication: Excellent technical writing skills, with the ability to author clear, comprehensive architecture designs, system documentation, and runbooks
What the job involves
We’re the infrastructure team behind Workday’s data analytics platform, responsible for building and operating the distributed systems that power analytics at scale across both public and private clouds
Our work supports a rapidly growing customer base, expanding global regions, and increasingly complex data workloads. As we evolve the platform, our focus is on delivering highly available, performant, and cost‑efficient infrastructure that can scale intelligently and operate reliably under pressure
As part of this team, you’ll help shape the next generation of compute infrastructure for Workday analytics. You’ll work on distributed systems that support massively parallel data processing, with a focus on performance, reliability, and simplicity at scale
We work closely with product, SRE, and engineering partners to solve real‑world problems—from enabling on‑demand compute for Spark workloads to improving system efficiency and observability. We value clear strategy, long‑term thinking, and a culture where engineers are empowered to own problems end to end
We’re building a scalable, multi‑tenant data platform that supports Apache Spark on both Kubernetes and YARN, serving diverse workloads across private and public clouds
Design, build, and debug distributed software systems that run across multiple platforms and environments
Develop tools and services that support efficient resource management, scheduling, and concurrency
Work with technologies like Apache Spark, Kubernetes, and container‑native infrastructure
Balance system design trade‑offs and collaborate across teams to deliver infrastructure that’s resilient, observable, and easy to operate
Support large‑scale data workloads with strong focus on performance, cost, and customer experience
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