Senior Analytics Engineer

Norm•New York City, NY

1d•Hybrid

About The Position

Norm builds AI-native professional services firms and the technology that powers them, with a mission to help expert professionals deliver better work at greater scale. Founded in 2023, Norm brings together experts across engineering, research, product, design, and professional practice. Its legal and compliance platform serves institutions representing more than $35 trillion in combined assets under management, and it also powers Norm Law, LLP, an affiliated AI-native law firm. The Senior Analytics Engineer owns the analytics data products that internal teams rely on to make decisions. The role turns prototypes and one-off analyses into trusted, production-grade models and metrics, including reusable models, business-facing metrics, access controls, tests, documentation, and guidance for Lightdash AI agents. It is a hands-on position combining modeling judgment with stakeholder discovery, reporting directly to the Director of Data and working with the data team and internal partners such as GTM and Finance. Norm expects team members to be fluent in AI and to use AI practically in their day-to-day work. Candidates should be prepared to discuss and demonstrate concrete examples of AI tools, workflows, and outcomes during interviews.

Requirements

At least 5 years of analytics engineering or equivalent experience.
Experience contributing to a production dbt project with tests, CI, incremental models, and documentation.
Strong dimensional modeling judgment, including designing conformed entities and facts at the correct grain and making 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; ability to verify their output and recognize 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, including treating 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 such as 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.

Responsibilities

Own analytics data products across internal business functions.
Design and maintain reusable intermediate models and business-facing gold models, metrics, access controls, tests, documentation, and Lightdash agent guidance.
Work with stakeholders to define requirements, validate results, and publish production data products.
Reconcile concepts that differ across source systems and publish mappings, unmatched records, assumptions, and quality checks so conflicts remain 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 data layers and partner with data engineering when the cause is below the serving layer.
Review modeling changes, pair with teammates, and help maintain shared standards.