Senior Analytics Engineer

This Role:

What You'll Do:

Core Qualifications

Nice to Haves

Compensation and Benefits

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.

Qualifications

• Core
• 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

Responsibilities

• 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:
• 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
• 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
• 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