Responsibilities

Develop data warehouse and data marts that transform raw data into high-performance, structured data models to power analyses & visualization tools for the Sales organization
Build “point-in-time” data models that enable trend analysis such as pipeline flow reporting
Enable Sales Ops users to perform their own ad-hoc analysis by maintaining clean, well-documented, and easy-to-query semantic layers
Design automated ingestion paths for manual spreadsheet data to ensure they are validated and integrated into the central data environment
Implement automated data quality checks and monitoring to ensure accuracy across all sales reporting
Maintain clear documentation of data lineage, business logic, and definitions for all sales-related metrics

Requirements

Engineering Mindset: A preference for "code-first" analytics—moving away from fragile manual processes toward reproducible, tested pipelines
Expert SQL & Python: Must be comfortable writing complex transformations and using Python for pipeline automation using tools such as Jupyter or Marimo
Deep understanding of schemas and fact/dimension tables to ensure intuitive self-service for data users
Experience with dbt or similar frameworks that prioritize version-controlled, modular data modeling
Familiarity with (or eagerness to learn) Customer lifecycles and CRM data structures (e.g. Salesforce)
Uncommonly driven to succeed and maniacally self-initiated; extreme attention to detail
Passion for clean energy and sustainability
Background in a quantitative field such as Mathematics, Economics, Finance, or Engineering is required; MBA experience is preferred