Analytics Engineer
Confirmed on the employer's own hiring board on Sep 23, 2026. First seen by Alion on Sep 23, 2026. Modal scores C on the Alion truth index.
About Us:
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, Ramp, 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 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,Luig i), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
About Modal Data:
We’re growing our Data team and are looking for our first few key hires to build self-serve data tools and drive business strategy in the right direction.
The mission of the Modal Data team is to make it easy to track company goals, make evidence-backed decisions, and prioritize the right work. We do this via:
Self-serve AI analytics tools (Hex, Snowflake)
Embedding with teams as a “data adviser”, providing strategic analysis and consulting
What You'll Do:
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 our tools and financial operations
Write data pipelines that power the operations of our business, such as our 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
What You Should Have:
SQL fluency, Python proficiency
Professional experience with at least 2 of the following tools: Snowflake, dbt, dlt, Modal, Hex, Posthog
Ability to extend their work beyond just data reporting and into action and impact
High attention to detail
Excellent and precise communicator
Strong personability and relationship building skills
Nice-to-Have:
Experience with AI products, especially LLM inference and sandboxes
Experience in fin ops, fraud, sales ops, or risk, especially in the context of AI (e.g. token cost optimization)
Project management skills
Strong presence in the data community online and offline