Staff AI Systems Engineer

Fivetran; Austin (TX); Hybrid; USD 183,711 - 229,638; Full time

Benefits offered by this job

100% employer-paid medical insurance

RSU stock grants

Professional development and training

PTO and paid time off

Monthly cell phone stipend

Mental health support platform

Job summary

Fivetran is seeking a Staff R&D Software Engineer for its AI team in a hybrid Austin, TX environment. You’ll explore retrieval techniques, prototype and ship production-grade features, and define technical direction across multiple teams. You’ll own reliability, QA, and mentoring, influencing product strategy and architecture.

You’ll work across backend, frontend, SRE, QA, and AI tooling, shaping an open data infrastructure that powers trusted AI agents and context layers in customer warehouses.

Qualifications

• 8+ years of programming experience across Python and/or Java, with the ability to move fluidly between back-end and front-end work.
• Trusted expert beyond your own team, able to exert influence and define technical direction across multiple teams
• Comfortable reading AI/ML research and turning promising findings into working prototypes, then production systems
• Strong product and market awareness — able to judge which emerging techniques are worth building versus which are hype
• Track record of taking ambiguous, high-level direction and independently defining and executing the concrete work to deliver it
• Experience with SQL and data warehouses (BigQuery, Snowflake, Databricks, or similar)
• Willing to work across the full stack: research, backend, frontend, SRE, and QA
• Writes well-structured, performant code and can dive into unfamiliar codebases to suggest improvements
• Experience using coding agents or similar AI tooling to speed up day-to-day engineering work
• Demonstrated ability to mentor other engineers and influence technical decisions beyond your immediate team
• Thrives in a startup-like environment with shifting priorities and a high degree of ownership

Responsibilities

• Research emerging techniques in retrieval, reasoning, and agentic AI, and decide what’s actually worth pursuing for Fivetran AI’s roadmap — then convince others
• Prototype new ideas quickly, then take the ones that prove out and turn them into shipped, production-grade features
• Define technical direction that spans multiple teams within Fivetran AI, ensuring architecture decisions made in one area don’t create problems in another
• Build and maintain both back-end and front-end systems for the Fivetran AI product — from Agents Schema pipelines to the Context Catalog UI
• Drive the AISQL capability forward: 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 testing and QA practices, and do hands-on QA 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
• Take high-level direction from product and leadership and independently define and execute the concrete plan to get there
• Mentor other engineers and raise the technical bar across the Fivetran AI team
• Contribute to hiring by participating in and helping shape the interview process

Skills

Python

Java

Frontend development

Backend development

SQL

Data warehouses

Mentoring

SRE

QA practices

Cloud AWS/GCP

Tools

Kubernetes

Docker

CI/CD

dbt

BigQuery

Job description

Fivetran is seeking a Staff R&D Software Engineer for its AI team in a hybrid Austin, TX environment. You’ll explore retrieval techniques, prototype and ship production-grade features, and define technical direction across multiple teams. You’ll own reliability, QA, and mentoring, influencing product strategy and architecture.

You’ll work across backend, frontend, SRE, QA, and AI tooling, shaping an open data infrastructure that powers trusted AI agents and context layers in customer warehouses.