About the Role
We're looking for an experienced Engineering Manager II to lead engineering for our Platform Services team - the team responsible for the shared infrastructure, internal tooling, and core services that other product teams build on. This is not a purely managerial role; you'll stay hands-on with architecture, code, and systems while owning team outcomes, delivery, and platform reliability at enterprise scale. 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.
- 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.
