HighLevel - Engineering Manager II
Requirements
• 10+ years of overall software engineering experience, including at least 2+ years in a Software Development Manager (or equivalent) role. • Expert-level proficiency in either Go, Python or Nodejs. • Prior experience managing a platform team, or having worked as an individual contributor on platform teams for at least 3+ years, at enterprise scale. • Strong hands-on orientation - comfortable reviewing code, debugging production issues, and going deep on architecture, not just managing processes. Candidates in a purely managerial (non-hands-on) role for the last 1+ year will not be considered a fit. • Deep, practical experience with GCP and/or AWS service management - provisioning, scaling, IAM, networking, and cost governance. • Demonstrated track record of driving cost optimization initiatives in an enterprise cloud environment, with measurable results. • Strong understanding of distributed systems architecture - consistency, availability, fault tolerance, scaling patterns, and trade-offs at scale. • Strong observability fundamentals - hands-on experience with tools like Prometheus, Grafana, ELK/OpenSearch, or equivalent, and a track record of building observability into systems from the ground up. • A data-driven approach to decision-making, prioritization, and performance reporting. • Proven experience in roadmap planning, estimation, and delivering complex initiatives on time. • Strong people management skills - hiring, mentoring, performance management - combined with the ability to collaborate effectively across engineering, product, and security teams. • Excellent verbal and written communication skills, with the ability to influence both technical and non-technical stakeholders. • AI-first mindset - actively using AI tools in engineering workflows in recent years; experience building or integrating agent-driven systems is a strong plus. • What You'll Lead: • A team of platform engineers building and operating shared services, internal platforms, and infrastructure used across multiple product lines. • Cloud infrastructure and service management across GCP/AWS, including provisioning, scaling, security, and cost governance. • Observability strategy - metrics, logging, tracing, and alerting - so the org has clear visibility into system health and performance. • Adoption of AI-driven engineering practices, including exploring agentic workflows and AI-assisted tooling to improve developer velocity and platform automation.
Responsibilities
• Architecture & Scale: Own and evolve distributed systems architecture for platform services, ensuring they're reliable, extensible, and performant under enterprise-scale load. • Cost & Efficiency: Drive continuous cost optimization initiatives across cloud infrastructure without compromising reliability or performance - with measurable, sustained impact. • Execution & Delivery: Own roadmap planning, estimation, and predictable delivery for the platform team, balancing tech debt, new capabilities, and operational stability. • Reliability & Operations: Establish strong observability and incident management practices; ensure the team can detect, diagnose, and resolve issues quickly, and drive blameless postmortems that lead to real fixes. • Data-Driven Decision Making: Use metrics and data to prioritize, evaluate trade-offs, and report on platform health, cost, and team performance. • People & Culture: Hire, mentor, and grow engineers; set technical standards; build a culture of ownership and craftsmanship within the team. • Cross-Functional Collaboration: Partner closely with product engineering teams, SRE/infra, security, and leadership to align platform capabilities with organizational needs. • Communication: Represent the platform team's priorities, risks, and progress clearly to engineering leadership and stakeholders across the org.
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