Senior Staff R&d Software Engineer, Fivetran AI

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

Senior Staff R&D Software Engineer to join Fivetran AI team. Focus on building the governed context layer for AI agents, including Agents Schema and Context Builder. Research emerging AI techniques, prototype, and ship production software. Role involves backend, frontend, SRE, and QA work, with a focus on reliability, scalability, and open standards for agent-ready data infrastructure.

What you'd actually do

  1. Identify which emerging AI research and techniques are worth pursuing, and set the agenda for Fivetran AI’s technical roadmap accordingly
  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 Fivetran AI and collaborating departments — product, data platform, and go-to-market — resolving architectural tradeoffs that cross department lines
  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. Set the long-term technical vision for capabilities like AISQL: natural language to SQL grounded in dbt metric definitions, executed natively against the warehouse

Skills

Required

  • Python
  • Java
  • SQL
  • dbt
  • LLMs
  • vector databases
  • BigQuery / Snowflake / Databricks
  • Kubernetes
  • React
  • TypeScript
  • backend development
  • frontend development
  • SRE
  • QA

Nice to have

  • MCP protocol

What the JD emphasized

  • agents are the new primary data consumers
  • fundamentally different requirements
  • explicitly codified, governed, and traceable
  • governed context layer
  • open standard
  • customer’s own data warehouse
  • managed service
  • keeps it filled and fresh
  • research emerging techniques
  • fast-moving AI landscape
  • real product and market understanding
  • decide which ideas are worth pursuing
  • take what you’ve learned and 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 department
  • judgment shapes technical direction
  • define your own direction rather than waiting for it
  • execute with the highest level of independence
  • operates like a startup within Fivetran
  • thrive on that range and ambiguity
  • rather than staying in one lane
  • Builds Infrastructure Agents Can Trust
  • mission to deliver the governed context layer that AI agents depend on
  • accurate semantic definitions
  • traceable lineage
  • data contracts
  • auditable history baked in from the start
  • Embraces Open Standards
  • portable, interoperable data infrastructure
  • Agents Schema
  • open formats (Iceberg, Delta Lake)
  • MCP-native interfaces
  • connector skills that work with any model and any compute
  • Scales Without Breaking
  • efficient at agent scale
  • unit costs deflate as volume grows
  • context retrieval is fast, accurate, and cost-controlled
  • no-nonsense tools
  • simplicity and effectiveness
  • back-end is built on Java, Python, Postgres, and Kubernetes
  • front-end is built on React and TypeScript
  • Python, Java, SQL, dbt, LLMs (Claude, ChatGPT, Gemini), vector databases, BigQuery / Snowflake / Databricks, MCP protocol, React, TypeScript, Kubernetes
  • Identify which emerging AI research and techniques are worth pursuing
  • set the agenda for Fivetran AI’s technical roadmap accordingly
  • Prototype new ideas quickly
  • take the ones that prove out and turn them into shipped, production-grade features
  • Define technical direction that spans Fivetran AI and collaborating departments — product, data platform, and go-to-market — resolving architectural tradeoffs that cross department lines
  • Build and maintain both back-end and front-end systems for the Fivetran AI product — from Agents Schema pipelines to the Context Catalog UI
  • Set the long-term technical vision for capabilities like AISQL: natural language to SQL grounded in dbt metric definitions, executed natively against the warehouse
  • Take ownership of production reliability across the platform: on-call rotation, incident response, and SRE work to keep the system trustworthy at scale
  • Set the bar for engineering quality, testing, and QA practices across the department, and do hands-on work yourself when it matters most
  • Use coding agents to automate the repetitive parts of the job, freeing up time for the research and design work that needs a human
  • Define your own priorities and roadmap based o

Other signals

  • building the governed context layer that solves this problem
  • Agents Schema, an open standard for storing agent-ready context directly in the customer’s own data warehouse
  • Context Builder, the managed service that keeps it filled and fresh
  • agents are the new primary data consumers
  • AISQL: natural language to SQL grounded in dbt metric definitions