Principal Distributed Systems Engineer - Observability

Your work days are brighter here. We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we’re shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, you’ll feel it. Not just in the products we build, but in how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm. We’re in this together, tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you’ll do meaningful work with Workmates who’ve got your back. In return, we’ll give you the trust to take risks, the tools to grow, the skills to develop and the support of a company invested in you for the long haul. So, if you want to inspire a brighter work day for everyone, including yourself, you’ve found a match in Workday, and we hope to be a match for you too. About the Team The Data Platform and Observability Engineering (DPOE) team is building Workday's next-generation, multi-petabyte scale Observability Platform. We own the libraries, distributed services, and infrastructure that power ingestion, storage, and query across the observability stack — Iceberg, ClickHouse, Tempo, Grafana, S3, Kafka, and Elasticsearch — including LangSmith for LLM/agentic tracing and evaluation serving traces, metrics, and logs for every workload at Workday. Our roadmap directly shapes how the company detects, diagnoses, and eventually predicts operational issues at scale. About the Role To own the technical vision and architecture for distributed tracing as a first-class pillar of Workday's Observability Platform, built on ClickHouse and/or Grafana Tempo , backed by a big-data pipeline (Kafka, Spark/Flink, Iceberg, Clickhouse, Tempo,S3) running on AWS . This is a hands-on, high-autonomy role for an engineer who can design and build multi-petabyte, low-latency tracing infrastructure end-to-end — and who is equally excited to help define where Observability AI goes next: using traces, logs, and metrics as the substrate for automated root-cause analysis, anomaly detection, and AI-driven incident triage. You'll set technical direction across multiple teams, mentor senior and staff engineers, and act as the primary architect and escalation point for the tracing subsystem — from ingestion and storage design through query performance and platform reliability. Architect and build Workday's distributed tracing platform on ClickHouse/Tempo, designed for multi-petabyte scale ingestion and sub-second interactive query performance. Own the big-data pipeline feeding tracing data — Kafka-based ingestion, Spark/Flink stream and batch processing, and Iceberg-on-S3 storage — including schema design, partitioning, compaction, and lifecycle management. Drive performance and scaling across ingestion and query paths: storage format optimization (Parquet/Iceberg), compression strategy, partitioning/indexing, and query engine tuning under real production load. Lead HA/DR design for tracing services — multi-region/multi-AZ resilience, failover, backup/restore, and recovery time/point objectives appropriate to a tier-1 platform. Design security architecture for the platform, including authentication/authorization (authn/authz) for multi-tenant data access across ingestion and query layers. Own operational excellence for distributed tracing: monitoring, logging, alerting, capacity planning, and participation in an on-call rotation for the platform. Evaluate and introduce new technologies — open source and cloud-native — that materially improve the platform's scalability, cost efficiency, or capability. Shape the future of Observability AI : Extend distributed tracing to LLM and agentic workflows using LangSmith and LangChain, enabling observability into multi-step agent execution, tool calls, and prompt/response chains. Evangelize the platform : publish best practices, mentor engineers across DPOE and partner teams, and act as a technical thought leader for the modern observability/data stack internally. Operate with high autonomy in a fast-moving, ambiguous environment — setting technical direction with minimal oversight while aligning with broader platform strategy. About You Basic Qualification  14+ years experience in software development engineering. 6+ years experience specifically focused on designing, building, and operating complex distributed system architectures, evidenced by successful deployment of systems with high availability (e.g., 99.9% uptime) and fault tolerance. 8+ years experience with at least two of the following programming languages (e.g., Java, Python, Go), including experience in writing production-level code for distributed systems. Bachelor’s degree in a relevant field such as Computer Science, Engineering, or a related discipline; a Master's degree (e.g., MS in Computer Science, Distributed Systems, or related field) is strongly preferred or equivalent practical experience. Other Qualification Expert-level ability in Algorithmic Thinking to architect highly efficient and scalable solutions for complex Deep expertise in API Development, including understanding of advanced API protocols or architectural patterns Deep understanding of Distributed Systems Software principles, like distributed consensus or fault tolerance mechanisms Proven ability to design and implement High Availability strategies for critical distributed systems Experience with LLM observability and tracing tools such as LangSmith, and familiarity with LangChain or similar agent orchestration frameworks Extensive experience with Large Scale Data Processing technologies and frameworks Deep understanding of Large Sca