Senior Data Analyst, Norm AI

• 5-7 years of data analytics experience, ideally with 1-2 years of data engineering exposure
• Strong SQL and Python skills, with hands-on experience in a modern data stack (dbt, Fivetran, Hex, Airflow)
• Experience working with cloud data warehouses such as Snowflake, BigQuery, or Redshift
• Proven ability to build analytics foundations or data models from scratch, especially in early-stage or scaling environments
• Experience supporting B2B SaaS products and understanding enterprise customer behavior
• Ability to translate complex business questions into technical data solutions
• Comfort working in small, fast-moving teams and wearing multiple data hats

Desirable

• As a Data Analyst at Norm, you will join our growing data team to build and scale our analytics capabilities. You will develop data models, product insights, and measurement frameworks that power our AI compliance engine and inform how we evaluate, understand, and optimize our product
• Working closely with our Director of Data and cross-functional partners in Product, Engineering, and Executive teams, you will translate complex compliance and legal engineering workflows into actionable insights. You will play a key role in establishing best practices, data foundations, and self-service analytics as our analytics function scales
• Build and maintain data models, pipelines, and help develop ELT processes that support product and business analytics
• Implement comprehensive product analytics to understand how enterprise clients use our AI-driven compliance workflows
• Develop measurement frameworks to evaluate AI model performance in real-world legal and compliance contexts
• Design and deliver self-service analytics capabilities using modern tools like Hex for analysis and visualization, dbt for semantic modeling
• Set up and maintain reliable data pipelines using orchestration platforms like Airflow
• Establish and uphold data governance practices aligned with enterprise security and regulatory requirements
• Build dashboards and KPIs that drive strategic product and business decisions
• What Success Looks Like - 30 Days
• Develop comprehensive understanding of our product and current data landscape
• Assist in setting up foundational data infrastructure and begin implementing core analytics tracking
• Document our data requirements and create a roadmap for analytics capabilities
• Establish relationships with key stakeholders across Product, Engineering, and Business teams
• What Success Looks Like - 60 Days
• Deploy production data pipelines and establish reliable data collection across the platform
• Launch initial set of product analytics dashboards for user behavior and AI model performance
• Begin analysis of compliance workflow patterns to identify optimization opportunities
• Create self-service analytics capabilities for the broader team