Job description

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 - Review modeling changes, pair with teammates, and help maintain shared standards WHAT WE'RE LOOKING FOR: CORE QUALIFICATIONS - 5+ years of analytics engineering or equivalent experience, including contribution to a production dbt project with tests, CI, incremental models, and documentation - Strong dimensional modeling judgment: you can design conformed entities and facts at the correct grain, and make clear decisions when source systems use conflicting definitions - Experience delivering a metrics or semantic layer used by both people and tools, with safe access controls for sensitive data - Familiarity with and regular use of AI coding agents. You verify their output and know when they are not the right tool - Strong SQL skills and enough Python experience to understand existing pipelines and build transformation tasks - Clear technical writing. You treat model documentation and metric definitions as part of the data product NICE TO HAVES - Experience with Lightdash, or another semantic layer managed as code - AWS knowledge and familiarity with tools like Athena, Iceberg, and Spark - Experience with legal, private equity, or financial services data, including restricted or client-sensitive information - Experience setting standards or mentoring on a small team COMPENSATION AND BENEFITS Base Salary: $175,000 to $215,000 per year The range displayed in this job posting reflects the minimum and maximum target for new hire salary for this position. Within the range, individual pay is determined by various factors, including job-related skills, experience, and relevant education or training. Equity: Included Benefits: 401(k) with employer match, health, dental, vision coverage, unlimited paid time off, free lunch in office daily, employee referral bonus program Relocation: Financial support to ease your move to New York City, designed to make the transition smooth and stress-free. WORK LOCATION Location: New York City Work model: Hybrid In-office expectation: 3–4 days per week To learn more about Norm Ai, visit our website norm.ai.

Requirements

Must-haves

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
Review modeling changes, pair with teammates, and help maintain shared standards
5+ years of analytics engineering or equivalent experience, including contribution to a production dbt project with tests, CI, incremental models, and documentation
Strong dimensional modeling judgment: you can design conformed entities and facts at the correct grain, and make clear decisions when source systems use conflicting definitions
Experience delivering a metrics or semantic layer used by both people and tools, with safe access controls for sensitive data
Familiarity with and regular use of AI coding agents. You verify their output and know when they are not the right tool
Strong SQL skills and enough Python experience to understand existing pipelines and build transformation tasks
Clear technical writing. You treat model documentation and metric definitions as part of the data product
Location: New York City
Work model: Hybrid
In-office expectation: 3–4 days per week

Nice-to-haves

Experience with Lightdash, or another semantic layer managed as code
AWS knowledge and familiarity with tools like Athena, Iceberg, and Spark
Experience with legal, private equity, or financial services data, including restricted or client-sensitive information
Experience setting standards or mentoring on a small team

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

airestsqlpythonaws

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off