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Lunar Energy

Operations BI Engineer (6 months contract)

Mountain View, CA$104k–$145.6kcontractmidAdded today

About this role

Build BI dashboards, internal tools, and workflow automations for manufacturing, supply chain, quality, and fulfillment operations at a fast-growing solar and battery company. You'll translate stakeholder needs into actionable dashboards and automations that drive operational decisions across a rapidly scaling business.

What you'll do

  • Design and maintain BI dashboards and KPI reporting for manufacturing, supply chain, quality, and fulfillment teams
  • Build internal tools, scripts, and workflow automations to replace manual status reporting and exception tracking
  • Extract, validate, and integrate data from ERP, MES, PLM, and other operational systems using SQL and Postgres
  • Connect business systems like Jira, Slack, Atlassian, and Google Workspace via APIs to automate workflows and reporting
  • Track and visualize operational KPIs including OEE, yield, downtime, cycle time, throughput, and fulfillment performance
  • Scope and prioritize ambiguous stakeholder requests into defined deliverables

What they're looking for

  • SQL and Postgres database querying
  • Grafana dashboard development and maintenance
  • Python scripting for automation and tool building
  • API integration (Slack, Atlassian, Google)
  • Data validation and ETL processes
  • Manufacturing and supply chain operations knowledge
  • Infrastructure-as-Code (nice to have)
  • AI-assisted coding tools like Claude Code or GitHub Copilot (nice to have)
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Lunar Energy

Lunar Energy builds hardware and software systems that require sophisticated integration, testing, and field deployment. The company is hiring Systems Test Engineers, Field Service Engineers, and Data Infrastructure Engineers to support product launches, system integration, and enterprise data platform operations.

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Likely interview questions

  • Walk us through a time you built a BI dashboard that directly influenced an operational decision—how did you scope the requirements and measure impact?
  • Describe your experience integrating data from multiple fragmented sources. How did you handle data quality and validation issues?
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