Who you are

• 6+ years of experience in Software Engineering, Distributed Systems or a related field
• 3+ years proficiency in at least two of the following programming languages: Java, Scala, Python
• 3+ years in cloud engineering or related field(s)
• Working knowledge of cloud-based infrastructure and managed services (AWS, GCP)
• Experience with at least one of these data engineering technologies: Apache Spark, Apache Iceberg, Kubernetes, Terraform AWS Cloud Infrastructure
• BSc or MSc in Computer Science/Computer Engineering or equivalent experience
• Experience in delivering a service from writing code to deploying in production: continuous integration (Jenkins), virtualisation (Docker), orchestration (Kubernetes, Terraform)
• Experience utilizing AI-Code Generation tools such as ClaudeCode, Windsurf, Cursor etc
• Experience creating scalable service endpoints to retrieve data
• Track record of working with logging, monitoring, metrics, stats technologies, such as: Grafana, Prometheus, Kibana, Hive, etc
• Proficient collaborating with teammates to design, maintain and improve sophisticated object-oriented software following clean code standard methodology
• A testing/quality approach - unit, system/integration and end-to-end testing, TDD, feature toggles, and canary deployments
• Exposure to operating system concepts covering memory and storage, threading and concurrency, networking and sockets, and process management
• An understanding and experience with topics related to performance and scale, security, availability, deployment and operations
• Experience being responsible for a service in production with experience of production triage and on-call

What the job involves

• Datahub is an AI/MLOps centered Data engineering team and our goal is to ingest, compute, and surface data driving key Workday applications across the company
• In this role, you will contribute to a cloud ops platform that enables ingestion of data from internal and external producers, computes and aggregate meaningful, curated data sets, the surfaces these sets to Workday Front-End components or as embedded contextual data for AI/ML inference generation and agentic workflows
• We utilize technologies like Kubernetes, Spark, Python, Java, XO, Terraform, Kubernetes, Iceberg, EMR, and Sagemaker