Analytics Engineer

Modal Labs; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Modal Labs in New York is building the data foundation to power AI-driven products and analytics. We are hiring for a data-focused role to shape the analytics stack, track key metrics, and empower teams with self-serve insights.

You will write data pipelines, work with Snowflake and dbt, and help turn data into business impact through rigorous analysis and clear communication.

Qualifications

• SQL fluency and Python proficiency.
• Experience with at least 2 of the following tools: Snowflake, dbt, dlt, Modal, Hex, Posthog.
• Excellent communicator with strong relationship-building skills.

Responsibilities

• Contribute to building the most modern analytics stack to support AI-driven self-serve analysis and key metrics tracking.
• Influence work on new products through product analytics tracking.
• Identify cost savings and optimization opportunities across tools and finance operations.
• Write data pipelines powering business operations (cloud compute economics, sales comp).
• Create foundational datasets for product use cases, financial reporting, and marketing campaigns.

Skills

SQL fluency

Python proficiency

Tools

Snowflake

dbt

dlt

Modal

Hex

Posthog

Job description

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,Luigi), 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