Principal Distributed Systems Engineer - Observability

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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, Click House, Tempo, Grafana, S3, Kafka, and Elasticsearch — serving traces, metrics, and logs for every workload  roadmap directly shapes how the company detects, diagnoses, and eventually predicts operational issues at scale.

To own the technical vision and architecture for distributed tracing as a first-class pillar of Workday's Observability Platform, built on Click House 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 Click House/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: partner with ML/AI stakeholders to define how tracing data feeds automated anomaly detection, root-cause analysis, and AI-assisted incident management.

Evangelize the platform: publish best practices, mentor engineers across DPOE and partner teams, and act as a technical thought leader…