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. The role requires a generalist approach, working on both backend and frontend systems, and driving capabilities like AISQL.

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
  • MCP protocol
  • React
  • TypeScript
  • dbt

Nice to have

  • research emerging techniques in retrieval, reasoning, and agentic AI
  • prototyping
  • backend and frontend systems development
  • AISQL capability
  • production reliability
  • SRE
  • QA
  • coding agents

What the JD emphasized

  • agents are the new primary data consumers
  • fundamentally different requirements
  • codified, governed, and traceable
  • research emerging techniques
  • ship it as production software
  • true generalist
  • wear whatever hat the moment calls for
  • prototyping
  • hardening a backend service
  • SRE or QA work
  • trusted expert
  • defining technical direction
  • high degree of independence
  • sound judgment
  • startup within Fivetran
  • thrive on that range and ambiguity
  • staying in one lane
  • governed context layer
  • accurate semantic definitions
  • traceable lineage
  • data contracts
  • auditable history
  • portable, interoperable data infrastructure
  • work with any model and any compute
  • efficient at agent scale
  • context retrieval is fast, accurate, and cost-controlled
  • no-nonsense tools
  • simplicity and effectiveness
  • production reliability
  • on-call rotation
  • incident response
  • SRE work
  • trustworthy at scale
  • testing and QA practices
  • hands-on QA work
  • coding agents to automate
  • research and design work that needs a human
  • high-level direction
  • independently define and execute

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