Staff R&d Software Engineer, Fivetran AI

Fivetran Fivetran · Data AI · Oakland, CA · Fivetran AI Department

Staff R&D Software Engineer for Fivetran AI team. Focuses on building a governed context layer for AI agents, including Agents Schema and Context Builder. Involves researching emerging AI techniques (retrieval, reasoning, agentic AI), prototyping, shipping production software, defining technical direction, and ensuring production reliability. Also drives AISQL capability and uses coding agents.

What you'd actually do

  1. Research emerging techniques in retrieval, reasoning, and agentic AI, and decide what’s actually worth pursuing for Fivetran AI’s roadmap — then convince others
  2. Prototype new ideas quickly, then take the ones that prove out and turn them into shipped, production-grade features
  3. Define technical direction that spans multiple teams within Fivetran AI, ensuring architecture decisions made in one area don’t create problems in another
  4. Build and maintain both back-end and front-end systems for the Fivetran AI product — from Agents Schema pipelines to the Context Catalog UI
  5. Drive the AISQL capability forward: natural language to SQL grounded in dbt metric definitions, executed natively against the warehouse

Skills

Required

  • Python
  • Java
  • SQL
  • Kubernetes
  • LLMs
  • vector databases
  • BigQuery / Snowflake / Databricks
  • React
  • TypeScript
  • research
  • prototyping
  • production software development
  • system design
  • backend development
  • frontend development
  • on-call rotation
  • incident response
  • SRE
  • QA
  • testing

Nice to have

  • dbt
  • MCP protocol

What the JD emphasized

  • agents are the new primary data consumers, and they have fundamentally different requirements
  • research emerging techniques in the fast-moving AI landscape
  • ship it as production software
  • wear whatever hat the moment calls for
  • thrive on that range and ambiguity rather than staying in one lane
  • Builds Infrastructure Agents Can Trust
  • Scales Without Breaking
  • research emerging techniques in retrieval, reasoning, and agentic AI
  • shipped, production-grade features
  • production reliability
  • coding agents to automate the repetitive parts of the job

Other signals

  • building the governed context layer that solves this problem
  • Agents Schema, an open standard for storing agent-ready context
  • Context Builder, the managed service that keeps it filled and fresh
  • research emerging techniques in retrieval, reasoning, and agentic AI
  • prototype new ideas quickly, then take the ones that prove out and turn them into shipped, production-grade features
  • Drive the AISQL capability forward