About the role
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Job Category
Software Engineering
Job Details
About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
The Experience
Design, build, and maintain the backend services, APIs, data-platform automation, and AI platform / agent systems behind our data products. This is a backend-heavy role: distributed Python services, streaming. LLM/agent runtimes on AWS, RAG pipelines, and the dbt/Airflow automation that powers a Snowflake-based lakehouse.
You will work inside the Developer Experience team, whose mandate is to build the tooling, “paved paths,” and automation that let internal data teams 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 (Bronze → Silver → Gold → Semantic Views).
- Transformation — dbt (Cloud on Fusion) with data contracts, tag-driven governance, and automated project-evaluation checks.
- Orchestration — Airflow on AWS MWAA; DAGs and schedules authored and deployed through platform tooling and CI (metadata driven).
- Developer tooling — an internal CLI and reusable utilities (dbt, Iceberg operations, Snowflake load, REST API based ingestions, etc.) that abstract the platform for data teams.
- Data quality & governance — Monte Carlo for data observability (monitors as code) and centralized governance (tagging, lineage, PII classification, cataloging).
- AI layer — Agent/MCP/Skill deployment framework, RAG, semantic router / broker agents, over our data products and catalog metadata; conversational agents in Slack; model access via AWS Bedrock / AgentCore, centralized LLM Gateway, and Snowflake Cortex.