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Salary: USD 175,000 - 215,000 / annual
Norm AI is an agentic law company that has pioneered Legal Engineering—a process enabling lawyers to build and supervise domain-specific AI agents using proprietary no-code software tools. The company's technology is deployed across many of the largest institutions globally, and Norm AI also operates Norm Law, LLP, an AI-native law firm advising leading institutions in private funds, private equity, venture capital, real estate, and financial regulation.
As Senior Analytics Engineer, you will own the analytics data products that internal teams depend on for decision-making. You will transform prototypes and one-off analyses into trusted, production-grade models and metrics. This is a hands-on role combining modeling judgment with stakeholder discovery, reporting directly to the Director of Data and collaborating closely with the data team, GTM, and Finance.
Key responsibilities include:
- Owning analytics data products across internal business functions
- Designing and maintaining reusable intermediate models, business-facing gold models, metrics, access controls, tests, documentation, and Lightdash agent guidance
- Turning ideas into production data products by working with stakeholders to define requirements, validate results, and publish them
- Reconciling concepts that differ across source systems and publishing mappings, unmatched records, assumptions, and quality checks
- Delivering changes through the full production process: SQL, dbt tests, Lightdash metadata, access rules, content validation, and AI agent instructions
- Diagnosing incorrect or stale results across data layers and partnering with data engineering when needed
- Reviewing modeling changes, pairing with teammates, and maintaining shared standards
The company emphasizes AI fluency across all team members. Successful candidates actively use AI in day-to-day work to support thinking, creation, and problem-solving, and should be prepared to demonstrate concrete examples of tools, workflows, and outcomes throughout the interview process.
QUALIFICATIONS:
Core Requirements:
- 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: ability to 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; ability to 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; treating model documentation and metric definitions as part of the data product
Nice to Have:
- 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
