Data Analyst
As a core member of our data team, you will be responsible for transforming raw data into strategic assets. This role is a hybrid of Data Engineering and Advanced Analytics, requiring you to not only move data but to architect it for complex business logic, such as churn analysis, billing cycles, and machine learning performance tracking.
Requirements
- SQL Expertise: Mastery of SQL, specifically for Snowflake and MySQL Workbench environments
- Workflow Automation: Proven experience with Airflow for orchestrating complex data pipelines
- Programming Skills: Proficiency in Python for data analysis, regression modeling, and infrastructure optimization
- Data Warehousing: Hands-on experience with Snowflake and AWS S3 for data storage and retrieval
- Business Intelligence: Skilled in using Superset for creating interactive dashboards and automated reporting
- Version Control: Consistent use of Git for managing code and data flow procedure lists
- Analytic Specialization: Experience in user analytics (churn, cohorts), project analytics (Blender vs. Maya), and financial billing analytics
- Data Structuring: Expertise in organizing and testing complex JSON outputs for downstream analytical use
- Documentation Standards: Ability to maintain organized technical wikis or documentation in platforms like Notion
Responsibilities
- Data Flow Management: Create and maintain automated data flows to acquire data from multiple sources (SQL Databases, Notion, Stripe, Auth0) into the Snowflake Data Lake
- Pipeline Orchestration: Utilize Airflow and S3 to manage and extend the data flow procedure list for automatic triggering of reports and dashboards
- Infrastructure Optimization: Organize Output JSON in Orchestrator v2 to provide necessary information for future analyses and conduct data testing
- Advanced Analytics Development: Create comprehensive analyses focused on customers, including active users, churn rates, cohort tables, and time to activation
- Predictive Modeling: Develop and maintain linear and polynomial regression formulas to estimate project processing times, including regular coefficient updates
- Performance Monitoring: Track Machine Learning steps to optimize infrastructure, processing time, and correlations with variables like resolution and FPS
- Billing & Revenue Tracking: Manage billing analytics using Stripe and Stripe Sigma to track MMR, revenue, and credit usage limits
- Reporting & Alerts: Design charts and dashboards in Superset and integrate automated alerts with Slack or email notifications
- Technical Documentation: Write and maintain detailed documentation for both data flows and analytics procedures within Notion
Data Analyst
What will be your next steps?
Quick non-technical conversation
Our initial conversation is a brief, non-technical discussion to understand your background and career aspirations. We're keen to learn about your communication style and how you approach teamwork and decision-making.
60 to 90 minutes technical interview
This in-depth technical assessment, lasting 60 to 90 minutes, is designed to evaluate your specific skills and expertise. We will present you with challenges relevant to our client's requirements.
Client interview
In this stage, you will meet directly with the client for a final technical discussion. This interview will be similar in format to our internal technical assessment, allowing the client to see firsthand how your expertise aligns with their specific project needs and team.
Offer
Congratulations on successfully completing our rigorous evaluation process. We are pleased to extend an offer and recommend you to our clients.