Analytics Engineer

About Norm Ai
Norm Ai, the agentic law company, has a client base with a combined $30 trillion in assets under management.

Norm Ai pioneered Legal Engineering, the process that empowers lawyers to build and supervise domain-specific AI agents with Norm’s proprietary suite of no-code software tools. Norm Ai technology is deployed inside many of the largest and most consequential institutions in the world.

Norm Ai is also the technology behind Norm Law, LLP, a separate but affiliated AI-native law firm built for the era of agentic AI. Norm Law’s attorneys advise leading institutions across private funds, private equity, venture capital, real estate, registered funds, and financial regulation, using the same legal intelligence platform that powers Norm Ai’s products.

AI Fluency:
Norm Ai prefers all team members to be fluent in AI. Successful candidates actively use AI in their day-to-day work to support thinking, creation, and problem-solving. They use it to improve the quality and speed of their work and to continuously refine how work gets done end-to-end.

Candidates should be prepared to demonstrate and discuss their AI usage throughout the interview process, including concrete examples of tools, workflows, and outcomes. We look for practical, hands-on experience, not theoretical familiarity.

This Role:

The Senior Analytics Engineer will own the analytics data products that our internal teams rely on to make decisions. You will turn prototypes and one-off analyses into trusted, production-grade models and metrics. That means reusable models, business-facing metrics, access controls, tests, documentation, and the guidance our Lightdash AI agents need to answer questions correctly.

This role reports directly to the Director of Data and works closely with the rest of the data team and with internal partners such as GTM and Finance. It is a hands-on role that combines modeling judgment with stakeholder discovery.

What You'll Do:

• Own analytics data products across internal business functions

• Design and maintain reusable intermediate models, business-facing gold models, metrics, access controls, tests, documentation, and Lightdash agent guidance

• Turn ideas into production data products by working with stakeholders to define requirements, validating the results, and publishing them

• Reconcile concepts that differ across source systems, and publish mappings, unmatched records, assumptions, and quality checks so conflicts stay visible

• Deliver changes through the full production process, including SQL, dbt tests, Lightdash metadata, access rules, content validation, and AI agent instructions

• Diagnose incorrect or stale results across the data layers, and partner with data engineering when the cause sits below the serving layer