Staff R&d Software Engineer, Fivetran AI

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

Fivetran is seeking a Staff R&D Software Engineer for their AI team to build the governed context layer for AI agents. This role involves researching emerging AI techniques, prototyping, and shipping production software. The engineer will define technical direction, build back-end and front-end systems, drive AISQL capabilities, ensure production reliability, and set testing standards. The role requires a generalist who can handle ambiguity and operate like a startup within Fivetran.

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

  • Research emerging techniques in retrieval, reasoning, and agentic AI
  • Prototype new ideas quickly
  • Ship production software
  • Define technical direction
  • Build and maintain back-end and front-end systems
  • Drive AISQL capability
  • Ensure production reliability
  • Set testing and QA practices
  • Use coding agents
  • Independent execution of plans

Nice to have

  • Experience with LLMs (Claude, ChatGPT, Gemini)
  • Experience with vector databases
  • Experience with BigQuery / Snowflake / Databricks
  • Experience with MCP protocol
  • Experience with React
  • Experience with TypeScript
  • Experience with Kubernetes

What the JD emphasized

  • agents are the new primary data consumers
  • fundamentally different requirements
  • explicitly codified, governed, and traceable
  • governed context layer
  • Agents Schema, an open standard
  • Context Builder, the managed service
  • research emerging techniques
  • ship it as production software
  • true generalist
  • wear whatever hat the moment calls for
  • prototyping a new retrieval technique
  • hardening a backend service
  • SRE or QA work
  • trusted expert beyond your own team
  • defining technical direction
  • high degree of independence
  • sound judgment
  • startup within Fivetran
  • thrive on that range and ambiguity
  • staying in one lane
  • Builds Infrastructure Agents Can Trust
  • governed context layer that AI agents depend on
  • traceable lineage
  • data contracts
  • auditable history
  • Embraces Open Standards
  • portable, interoperable data infrastructure
  • Scales Without Breaking
  • efficient at agent scale
  • context retrieval is fast, accurate, and cost-controlled
  • no-nonsense tools
  • simplicity and effectiveness
  • Python, Java, SQL, dbt, LLMs (Claude, ChatGPT, Gemini), vector databases, BigQuery / Snowflake / Databricks, MCP protocol, React, TypeScript, Kubernetes
  • Research emerging techniques in retrieval, reasoning, and agentic AI
  • Prototype new ideas quickly
  • shipped, production-grade features
  • Define technical direction that spans multiple teams
  • architecture decisions
  • Build and maintain both back-end and front-end systems
  • Agents Schema pipelines
  • Context Catalog UI
  • Drive the AISQL capability forward
  • natural language to SQL
  • dbt metric definitions
  • executed natively against the warehouse
  • ownership of production reliability
  • on-call rotation
  • incident response
  • SRE work
  • trustworthy at scale
  • Set the bar for testing and QA practices
  • hands-on QA work yourself
  • coding agents to automate
  • research and design work that needs a human
  • high-level direction
  • independently define and execute

Other signals

  • AI agents are the new primary data consumers
  • 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