Jobs

Analytics Engineer

Modal

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Location
New York
Track
Data Scientist
Salary
$150K–$250K / yr
Posted
September 23, 2026
Source
Ashby

Job description

About Us

Our customers include category-defining companies like 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.

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

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