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Pear VC

AI-Native Data Engineer - TrueMeter

San Francisco Bay AreafulltimemidAdded 1 month ago

About this role

TrueMeter is seeking an AI-Native Data Engineer to develop and manage data infrastructure that transforms energy billing into a fixed-price subscription model. This startup role involves building reliable data pipelines, databases, and APIs to support their AI Energy Agent service in a rapidly evolving environment.

What you'll do

  • Build high-reliability data pipelines and APIs for utility and energy source integration
  • Architect databases and systems for time-series energy data ingestion
  • Design scalable GCP infrastructure for efficiency
  • Develop infrastructure for coding agents and AI workflow automation
  • Establish CI/CD and observability practices for fast product delivery
  • Document and communicate technical work and its business impact

What they're looking for

  • Experience with Python and TypeScript
  • Familiarity with GCP and SQL/NoSQL
  • Strong problem-solving skills
  • Effective communication with technical and non-technical teams
  • Ability to work independently in a startup environment
  • Passion for AI and LLM technologies
  • First-principles thinking
  • Agility in adapting to changing priorities

Benefits

  • Significant ownership of projects
  • Fast-paced work environment
  • Opportunity to design lasting systems
  • Impactful contributions to energy management
  • Flexible hybrid working arrangement
  • Collaboration with experienced founders
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Pear VC

Pear VC invests in and supports AI-driven enterprise software companies building automation platforms, compliance systems, and specialized tools across industries like financial services, insurance, and software development. The portfolio companies are hiring founding and senior engineers to design core infrastructure, build AI systems, and develop scalable backend platforms alongside their leadership teams.

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Likely interview questions

  • Walk us through a production data pipeline you've built end-to-end. What tools did you use, and how did you handle reliability and scaling?
  • Tell me about a time you had to make a tool choice (database, framework, infrastructure) based on first principles rather than what was trendy. What was your reasoning?
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