Software Development Engineer(Distributed Systems)

Workday•Pleasanton, CA

1d•$123,900 - $222,000•Hybrid

About The Position

Workday is seeking a Software Development Engineer to join the Data Platform and Observability Engineering (DPOE) team. This team is responsible for building Workday's next-generation, multi-petabyte scale Observability Platform, including the libraries, distributed services, and infrastructure for ingestion, storage, and query across the observability stack. The role involves building and scaling core features of Workday’s distributed tracing platform, working with technologies like ClickHouse, Grafana Tempo, Kafka, Spark/Flink, Iceberg, S3, and Elasticsearch on AWS. This is a hands-on role for an engineer focused on building resilient backend services and growing expertise in distributed systems and big data, contributing to Observability AI.

Requirements

5+ years experience in software development engineering.
3+ 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.
5+ years experience with at least two of the following programming languages 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.
Strong ability in Algorithmic to build highly efficient and scalable solutions for complex high-throughput data ingestion and sub-second query performance challenges.
Solid experience in API Development, including an understanding of gRPC, REST, and OpenTelemetry (OTLP), with practical experience designing and building scalable distributed APIs for observability data.
Strong understanding of Code Testing methodologies, such as distributed load testing and integration testing, and experience contributing to end-to-end telemetry pipeline testing and CI/CD automation.
Solid understanding of Distributed Systems Software principles, including data partitioning, eventual consistency, and fault tolerance mechanisms, with hands-on experience in Kafka, Spark, Flink, or ClickHouse.
Experience implementing and maintaining High Availability strategies for critical distributed systems, including multi-AZ deployments, robust retry mechanisms, and automated failover.
Practical experience with Large Scale Data Processing technologies and frameworks such as Apache Kafka, Spark, Flink, and Apache Iceberg within complex distributed architectures.
Good understanding of Large Scale Systems design principles, including distributed data sharding, replication, and query optimization, and experience working on observability pipelines or data lake platforms.
Strong understanding of Object-Oriented Design (OOD) principles and architectural patterns for building highly scalable and maintainable distributed systems.
Experience with Source Control Management (SCM) tools such as Git and GitHub/Bitbucket, and following best practices for collaborative distributed development workflows.
Strong understanding of System Security principles and best practices relevant to securing distributed environments, including mutual TLS (mTLS), multi-tenant authorization (authz), and data encryption.
Proven ability to actively collaborate within and across distributed software development teams and contribute constructively to architectural discussions and system designs.
Strong skills in creating Technical Writing Documentation for runbooks, system design specs, and API documentation related to distributed systems architecture and design.

Responsibilities

Develop the Platform: Write high-quality, well-tested code to build and scale features for the distributed tracing platform on ClickHouse/Tempo, focusing on robust ingestion and fast query execution.
Maintain Big-Data Pipelines: Build and support high-throughput data pipelines using Kafka, Spark/Flink, and Iceberg-on-S3.
Tune and Optimize: Write optimized code and participate in profiling and resolving latency or throughput issues in production environments.
Ensure Reliability: Implement robust error handling, retries, and failover mechanisms to ensure high availability for tracing services.
Secure the Data: Apply necessary security controls to ensure multi-tenant data access is properly handled according to platform architecture.
Operational Support: Maintain the health of the platform through comprehensive monitoring, logging, and alerting, and participate in the team’s on-call rotation.
Support Observability AI: Build reliable data ingestion paths that will serve as the foundation for AI-driven anomaly detection and root-cause analysis.
Collaborate and Learn: Write clear technical documentation, participate actively in system design reviews, and work closely with Senior and Principal engineers to level up your distributed systems knowledge.