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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.
Job Responsibility:
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
Requirements:
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
AI fluency: active use of AI in day-to-day work to support thinking, creation, and problem-solving
Nice to have:
Experience working in regulated industries such as FinTech, RegTech, or compliance-heavy environments
Background in data science, statistics, or applied modeling beyond standard reporting
Familiarity with legal, compliance, or enterprise risk workflows
Prior experience scaling analytics in a rapidly evolving startup environment