Senior Analytics Engineer, Genie AI

• The ideal candidate will have 5 years+ experience and:
• Advanced SQL: Expert-level SQL for complex data modeling and transformation
• Data modeling/transformation: Strong experience with dbt for transforming raw data into analytics-ready datasets
• Cloud data warehouses: Experience with BigQuery (preferred) or Snowflake/Redshift
• Business communication: Proven ability to translate data insights into business outcomes
• Data visualization: Experience creating intuitive dashboards that tell clear data stories
• BI tools: Proficiency with modern tools like Looker, Holistics, PowerBI or Tableau
• Metrics definition: Experience defining standardized business metrics and dimensions
• Python: Basic programming skills for data transformation and analysis
• Version control: Experience with Git for managing data transformation code
• Product Analytics: Experience with tools like Segment, Snowplow, Mixpanel or Amplitude
• (Don’t worry about ticking every single box on the list to be considered for this role.)

Desirable

• As a Senior Analytics Engineer at Genie AI you’ll get the chance to immerse yourself within the world of law, legal contracts and AI at a fast paced and autonomous start up
• We’re big enough to disrupt the legaltech industry, but small enough that you can make a massive impact on the team
• Some of your key day to day duties will include:
• Design, build, and maintain the data models which power business insights
• Develop metrics frameworks and dimensions that standardize how we measure business performance
• Work with stakeholders across marketing, sales, product, and operations and machine learning to translate business questions into data solutions
• Enable key stakeholders to share and present data insights across the business
• Enabling robust experimental results that removes opinion from discussion - this will ensure high impact growth / work is done
• Build and maintain a semantic layer that makes data accessible to non-technical users
• Create documentation and training to enable self-service analytics across teams
• Implement and maintain dbt models that transform our raw data into reliable, tested datasets
• Help establish data governance and quality practices as we scale
• Over your first 90 days you can expect us to help point you in the right direction to set you up for success
• Month 1: Understand business needs, audit data sources (Mixpanel, Segment, MongoDB, BQ etc.), map stakeholder requirements and develop a roadmap towards a simple but modern data stack. You’ll create a few high-value dashboards by working and playing with the data we already have
• Month 2: Implement core data models in BigQuery (probably) or Snowflake/Databricks, creating initial dashboards, establish a metrics framework, and set up dbt transformations for key entities
• Month 3: Enable self-service analytics, implement automated reverse ETL for marketing activation campaigns, refine dashboards based on feedback, and deliver actionable insights to growth, sales, finance and product stakeholders
• We’ll continually develop and measure success on the below criteria:
• Insight Activation: Business decisions across growth, product, sales and strategy are consistently made using data from our analytics platform
• Self-Service Analytics: Teams can answer their own data questions without your involvement using well-designed dashboards and data models
• Single Customer View: Creation of a unified customer data model that connects behavior across platforms and enables personalization within both growth and marketing comms, and product/ML via AI
• Metrics Standardization: Core business metrics are clearly defined, documented, and consistently calculated across the company (grey areas are known to everyone)
• Data Culture: Increased use of data in decision-making, with more teams requesting access to and using analytics tools
• Technical Foundation: Implementation of a sustainable, documented modern data stack that can evolve with our business needs
• We're excited to discuss with you the specific data problems we're working to solve – from unifying user journey data across multiple touchpoints to creating actionable customer segments that drive growth
• You'll help us answer questions like:
• What behaviors predict our highest-value customers?
• How do we measure product adoption across 120,000+ companies and a range of AI features?
• What engagement patterns lead to contract renewals?
• You'll work with data from Mixpanel, MongoDB, Segment, Stripe and more to build a unified view that powers everything from marketing campaigns to product decisions
• This role offers the rare opportunity to shape our entire data strategy while delivering immediate business impact. We'd love to dive deeper into these challenges with you during our conversations

• Step 1: Meet our Talent Acquisition Lead, Char, to assess your motivations and baseline skills
• Step 2: Complete our take home task
• Step 3: Business / Analytics interview with Alex Denne (our Head of Growth) and Rafie Faruq (our Co-Founder & CEO)
• Step 4: Engineering interview with Nitish Mutha (our Co-founder & CEO) and Emile Joubert (our Snr Software Engineer - Machine Learning)
• Step 5: Culture interview with Char (Talent Acquisition Lead) and Rosie Dent-Spargo (our Product Design Lead)
• For Lead level roles and above, we collect blind references to set you up for success. These insights help us ensure a smooth transition into our team and foster meaningful collaboration between you and your manager from day one
• We can’t wait to meet you! Bring your authentic self, and get ready to explore our culture, team events, and big mission. We're excited to discover what makes you and us unique