# Analytics Engineer at Modal > Onsite · United States · Unspecified · Data engineering · open. Source: https://alderhq.app/jobs/ashby%3Amodal%3Aa518f2cd-e0c7-4105-9004-d7971f7b1daf (read 2026-11-05). ## Facts (observed) - Company: [Modal](https://alderhq.app/companies/modal) - Location text: New York; Stockholm; San Francisco - Where it can be done from (inferred): Onsite · United States (evidence: "Location: New York; Stockholm; San Francisco") - Posted: 2026-09-23 (43 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/modal/a518f2cd-e0c7-4105-9004-d7971f7b1daf - Skills listed (inferred): Python, Docker, Snowflake, dbt, SQL, LLMs ## Pay and sponsorship - Posted pay: $150k–$250k per year (from the listing's pay field) - Market band for similar posted roles (New York, n 17): p25 $152k, median $190k, p75 $210k - H-1B filings at this company for similar roles, FY26: 5, median offered wage $200k ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance)) ## Description (the employer's text) ### 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 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've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. 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. ### The Role 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 Work On 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 ### You May Be a Fit If You’re fluent in SQL and proficient in Python You have professional experience with at least 2 of the following tools: Snowflake, dbt, dlt, Modal, Hex, PostHog You extend your work beyond just data reporting and into action and impact You have high attention to detail You’re an excellent and precise communicator You’re personable and great at building relationships ### 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Amodal%3Aa518f2cd-e0c7-4105-9004-d7971f7b1daf and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.