Senior Analytics Engineer

• 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
• 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
• 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

Benefits

• Competitive salary and equity
• 401(k) plan with a highly competitive employer matching contribution
• Top-tier health, dental, hospital, accident and vision insurance
• Free lunch provided when working from the office
• Paid parental leave
• State bar fee reimbursement
• Relocation reimbursement to move to NYC

• Familiarity with and regular use of AI coding agents. You verify their output and know when they are not the right tool
• Experience delivering a metrics or semantic layer used by both people and tools, with safe access controls for sensitive data
• 5+ years of analytics engineering or equivalent experience, including contribution to a production dbt project with tests, CI, incremental models, and documentation
• 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
• 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 with Lightdash, or another semantic layer managed as code
• Experience with legal, private equity, or financial services data, including restricted or client-sensitive information
• AWS knowledge and familiarity with tools like Athena, Iceberg, and Spark
• Experience setting standards or mentoring on a small team