Full-Time Senior Analytics Engineer

Transform how the legal industry operates with data. Build your own data pipelines and transformations, using a modern data stack (including dbt) to create business value.

Work autonomously, from deep data analysis to providing strategic, commercial, and customer insights.

Your Purpose: Enable a self-service data culture where teams access insights independently. Genie AI is backed by $17.8M Series A funding from Google Ventures and Khosla Ventures, with a vision to make law accessible to everyone.

Your Manager and Team: Organize in pods, focusing on impactful customer work. Structure prioritizes impact, autonomy, and velocity. You'll report to the Head of Growth, Alex Denne, and collaborate with the Co-Founder & CTO, Nitish.

Your Role: Immerse yourself in the world of law, legal contracts, and AI at a fast-paced startup. Design, build, and maintain data models that power business insights. Develop metrics frameworks and dimensions for measuring business performance. Collaborate with stakeholders across marketing, sales, product, and machine learning to translate business needs into data solutions.

Key Responsibilities:

• Design, build, and maintain data models.
• Develop metrics frameworks.
• Translate business questions into data solutions.
• Enable stakeholder data sharing and presentation.
• Ensure robust experimental results.
• Build and maintain a semantic layer for non-technical users.
• Create documentation and training for self-service analytics.
• Implement and maintain dbt models.
• Establish data governance and quality practices.

First 90 Days:

1. Understand business needs and data sources.
2. Map stakeholder requirements and create a roadmap.
3. Develop high-value dashboards.
4. Implement core data models.
5. Create initial dashboards and a metrics framework.
6. Set up dbt transformations.
7. Enable self-service analytics.
8. Implement automated reverse ETL.
9. Refine dashboards and deliver actionable insights.

Success Criteria:

• Insight Activation: Business decisions consistently use data.
• Self-Service Analytics: Teams independently access data insights.
• Single Customer View: Unified customer data model enabling personalization.
• Metrics Standardization: Clear, documented, and consistent core metrics.
• Data Culture: Increased data-driven decision-making.
• Technical Foundation: Sustainable, documented modern data stack.

Company Culture: We value autonomy, iterative development, and a generalist approach to analytics.

Skills Required:

• Advanced SQL
• Data modeling and transformation experience, including dbt
• Cloud data warehouses, including BigQuery (preferred)
• Business communication
• Data visualization and BI tools (Looker, Holistics, PowerBI, Tableau)
• Metrics definition
• Basic Python programming
• Version Control (Git)
• Product analytics tools (Segment, Snowplow, Mixpanel, Amplitude)

Nice to Haves:

• Familiarity with cloud platforms (GCP preferred)
• Data quality testing and validation experience
• Experience with ETL/ELT tools (Fivetran, Stitch)
• Understanding of reverse ETL

Interview Process: Includes talent acquisition assessment, a take-home task, business/analytics interviews, engineering interviews, and a culture interview.

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