Software Development Engineer
Posted
Summary
Builds and operates Workday's AI agent metrics platform, landing telemetry into Apache Iceberg via dbt and extending the MetricsFlow semantic layer. Day-to-day work includes CI/CD schema governance, multi-tenant data isolation, low-latency metric serving, and API development using Python, SQL, and distributed data frameworks.
- The Agent Measurement & Forecasting (AMF) team builds Workday’s single source of truth for how the company measures and forecasts AI agent performance
- We own the data pipeline, metric governance, and semantic layer that turn raw agent telemetry into consistent, auditable metrics used across Agents
- Our stack runs on dbt, Apache Iceberg, and MetricsFlow, backed by CI/CD schema enforcement, so every metric we publish carries the same rigor as production code
- As Workday scales via a broader agentic transformation, the metrics AMF governs are what let the company trust, and act on, how its agents are performing
- As a Software Engineer, you will work towards technical design and execution for core components of AMF’s metrics platform
- You’ll build and operate the pipeline that lands agent telemetry into Iceberg via dbt, enforce schema contracts through CI/CD gates, and extend the MetricsFlow semantic layer that serves governed metrics to the Product Data Store and downstream consumers
- This is a hands-on role: you’ll work on multi-tenant data isolation, low-latency query performance for metric serving, and the migration path from today’s per-tier governance model
- You will drive engineering excellence within the team and act as a domain expert for pipeline design, schema governance, and semantic-layer serving
- Bachelor’s degree in a relevant field such as Computer Science, Engineering, or a related discipline, or equivalent practical experience
- 5+ years experience in software development engineering including designing, developing, and deploying software solutions
- 3+ years experience in software development engineering, with production-level experience in: Python and SQL
- Solid understanding of Object-Oriented Design (OOD) principles and architectural patterns for scalable, maintainable data systems
- Experience with source control management (Git, GitHub/Bitbucket) and collaborative distributed development workflows
- Strong grasp of distributed systems principles: data partitioning, eventual consistency, and fault tolerance
- Solid experience with dbt, Apache Iceberg, and semantic-layer tooling (e.g., MetricsFlow, dbt Semantic Layer, Cube) for governed metric definition and serving
- Practical experience with API development (gRPC, REST) for serving metrics and data products to downstream consumers
- Experience with large-scale data processing frameworks such as Spark, Flink, or Kafka within complex distributed architectures
- Proven ability to collaborate across distributed teams and contribute constructively to architectural discussions and system design
- Strong understanding of data governance and schema contract enforcement, including CI/CD schema gates and automated data quality checks
- Understanding of multi-tenant data isolation and security principles, including data encryption and tenant-scoped authorization, relevant to governing tenanted (Tier 0) vs. non-tenanted (Tier 1) data
- Strong technical writing skills for design specs, data contracts, and API documentation