Principal Backend Engineer, AI Data Platform

salesforce.com, inc.; San Francisco (CA); On-site; USD 238,000 - 345,000; Full time

Job summary

Salesforce is seeking a backend-focused engineer to design, build, and maintain the data platform's services, with emphasis on Python/Go/Java and scalable architectures in AWS. You will work inside the Developer Experience team, delivering tooling and automation that makes data products discoverable and governed.

Responsibilities include building RAG pipelines, integrating LLMs, and ensuring reliability with Terraform, MWAA/Airflow, and CI/CD.

Qualifications

• Extensive experience designing and maintaining production backend services at scale.
• Expert-level proficiency in Python, Go, and Java.
• Experience with REST and/or gRPC APIs and streaming.
• Strong data modeling with SQL and data warehouses (Snowflake).
• Hands-on with AWS and Terraform; CI/CD with GitHub Actions.

Responsibilities

• Backend services: Design, build, and maintain backend services in Python/Go to support Data Platform Services.
• Develop abstraction layers for data domain customers via custom utilities.
• Maintain infrastructure using Terraform for AWS accounts.
• Build RAG pipelines and tool-calling AI agents for data products.
• Integrate model providers with LLM Gateway and ensure observable outputs.
• Stand up eval harnesses, guardrails, and tracing.
• Data-platform automation with dbt and MWAA/Airflow.
• Extend and maintain the platform CLI.
• Identity & security: OAuth/OIDC flows and IAM enforcement.
• Reliability & delivery via Terraform, GitHub Actions, CI/CD.
• Set technical direction and code reviews.

Skills

Python

Go

Java

REST APIs

AWS

Airflow

Snowflake

Tools

dbt

Snowflake

Iceberg

Terraform

GitHub Actions

AWS Bedrock

Job description

Salesforce is seeking a backend-focused engineer to design, build, and maintain the data platform's services, with emphasis on Python/Go/Java and scalable architectures in AWS. You will work inside the Developer Experience team, delivering tooling and automation that makes data products discoverable and governed.

Responsibilities include building RAG pipelines, integrating LLMs, and ensuring reliability with Terraform, MWAA/Airflow, and CI/CD.