Sales Technical Analytics Intern

🕒 May 19

🇺🇸 United States – Remote

💵 $20 - $25 / hour

👨‍🎓 Internship

⚪️ Entry-level

🤑 Sales

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Voltus

WebsiteLinkedInAll Job Openings

201 - 500 employees

⚡ Energy

🏢 Enterprise

🤝 B2B

Energy • Enterprise • B2B

Voltus is a leading virtual power plant operator and distributed energy resource technology platform. The company pays energy users, including commercial, industrial, and residential sectors, to reduce or shift their electricity usage in response to grid stress, high prices, and high emissions. Operating across all nine wholesale power markets in the US and Canada, Voltus provides demand response solutions that create new revenue streams for energy users while contributing to grid reliability and sustainability. Voltus also offers real-time tracking of cash earnings, payments, energy usage, and avoided CO2 emissions, and has a focus on advancing equitable access to revenue opportunities related to the energy transition.

📋 Description

• 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