Lead Software Engineer

Quick Facts

• Role: Lead Software Engineer (Senior individual contributor)

• Location: US Remote

• Product focus: Demand Response Optimization Program (DROP)

Description

DROP is a client-facing platform that helps utilities plan and evaluate electricity demand management programs. It brings together customer, program, device, and energy-usage data to support participant assignment, operational planning, evaluation, and continuous improvement. As the Lead Software Engineer, you will build secure, reliable, maintainable software while contributing technical improvements and AI-first engineering practices across the platform.

Responsibilities

• Design, develop, test, document, implement, and maintain DROP components across the user interface, APIs, data services, and analytical workflows

• Build capabilities for customer and device assignments, cohorting, program planning, telemetry processing, baselines, performance analysis, and reporting

• Refactor and modernize existing code to improve maintainability, performance, modularity, security, and reuse

• Lead design decisions, code reviews, troubleshooting, and implementation guidance

• Mentor contributing engineers and analytical developers (with formal people-management responsibility)

• Partner with product leadership, utility consultants, and analytical specialists to translate user needs into technical requirements and implementation plans

• Provide technical options, estimates, dependencies, and delivery risks for roadmap and release decisions

• Support client implementations, technical discovery, demos, proposals, scopes of work, and solution reviews

• Improve automated testing, CI/CD, logging, monitoring, error handling, data validation, deployment practices, and operational documentation

• Develop and maintain data-intensive Python and SQL workflows in Microsoft Azure and Databricks

• Integrate DROP with utility data sources, DERMS platforms, APIs, telemetry feeds, storage services, Microsoft Fabric, Power BI, and client environments

• Collaborate with data engineers on reliable schemas, data-quality controls, and secure configuration patterns

• Apply AI-assisted engineering tools to improve development and delivery efficiency while maintaining accountability for correctness and quality

• Evaluate AI-generated code and outputs through review, testing, validation, and monitoring; identify opportunities for generative AI, agentic workflows, machine learning, or automation

Benefits

• Flexible workplace arrangements and work-life balance

• Donation matching and volunteer opportunities

• Tuition reimbursement and access to professional development resources

• 401k matching and Employee Stock Purchase Plan