Description
Principal Software Engineer, Platform Services (PMTS)
Design, build, and maintain backend services, APIs, data-platform automation, and AI platform/agent systems. This is a backend-heavy role with distributed Python services, streaming, LLM/agent runtimes on AWS, RAG pipelines, and dbt/Airflow automation powering a Snowflake-based lakehouse. You will work in the Developer Experience team, building tooling, paved paths, and automation for internal data teams to ship discoverable, governed, and consumable data products quickly and safely.
About the platform you'll work on
- Lakehouse & warehouse: Snowflake with Apache Iceberg tables in a medallion architecture.
- Transformation: dbt with data contracts, tag-driven governance, and automated project-evaluation checks.
- Orchestration: Airflow on AWS MWAA.
- Developer tooling: internal CLI and reusable utilities.
- Data quality & governance: Monte Carlo for data observability.
- AI layer: Agent/MCP/Skill deployment framework, RAG, semantic router/broker agents.
- Cloud & delivery: AWS (ECS Fargate, Lambda, API Gateway, DynamoDB, S3, IAM, etc.), Terraform IaC, GitHub Actions CI/CD.
Responsibilities
- Design, build, and maintain backend services in Python/Go.
- Build RAG pipelines and tool-calling AI agents for data products.
- Integrate model providers with LLM Gateway.
- Stand up eval harnesses, guardrails, and tracing for agent quality, latency, and cost.
- Build and maintain data-platform automation.
- Extend the platform CLI and shared utilities.
- Implement auth and identity.
- Enforce multi-tenant isolation and per-user identity/RBAC.
- Own service reliability and delivery.
- Set technical direction.
Required skills
- 15+ years designing, building, and maintaining production backend services at scale.
- Expert-level Python/Go/Java.
- Service & API design.
- Data layer: SQL, data modeling, query optimization.
- Server-side patterns.
- Performance & reliability.
- Observability.
- OOP.
- Data structures & algorithms.
- Concurrency & async programming.
- Testing.
- Cloud & infrastructure: production AWS, Terraform, GitHub Actions.
- Distributed systems.
- Security: OAuth/OIDC, authn/authz.
- AI/LLM engineering.
Nice to have
- dbt, Airflow/MWAA, Snowflake hands-on experience.
- MCP and/or applied RAG.
- Slack platform.
- Data observability.
- Fine-tuning/adaptation, semantic caching.
- Experience adding AI capabilities to existing production systems.
- Iceberg / open table formats.
- Tableau or other BI integration.