Analytics Engineer

New York · Stockholm · San Francisco · Full-time · Onsite · Posted Sep 23, 2026

At a glance

AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.

Modal is looking for its first few key hires on the Data team to build self-serve data tools and drive business strategy.
The role involves building a modern analytics stack to support AI-driven self-serve analysis, key metrics tracking, and external customer reporting.

Contribute to building the most modern analytics stack in Data today to support AI-driven self-serve analysis, key metrics tracking, and external customer reporting
Influence work on new products like LLM Inference Endpoints through product analytics tracking
Identify millions of dollars of cost savings and optimization across tools and financial operations
Write data pipelines that power the operations of the business, such as cloud compute economics or sales comp
Create foundational datasets that can be used by people and AI tools to answer questions around product use cases, financial reporting, and marketing campaigns

SQL fluency
Python proficiency
Professional experience with at least 2 of the following tools: Snowflake, dbt, dlt, Modal, Hex, Posthog
Ability to extend work beyond just data reporting and into action and impact
High attention to detail
Excellent and precise communicator
Strong personability and relationship building skills