Lead Engineer, Stream

• The team needs a strong engineering leader who comes from a software engineering background, can own technical decisions independently, and can make infrastructure move faster without introducing big-org process dependencies
• Software engineering background — you built things, not just configured them. Backend or platform coding experience in Go or Python (not scripting-only)
• Strong AWS or GCP depth — production ownership, not just certifications. Experience with cloud cost optimization at scale with measurable outcomes
• Distributed systems and database architecture — real schema decisions, replication trade-offs, latency budgets. Not just "ran Kubernetes"
• Hands-on leadership — you can lead a project and a PR in the same week. Comfortable in a team of 3–5, not dependent on large-org support structures
• AI tooling fluency — you use AI tools actively in your engineering workflow. We screen for applied adoption, not familiarity. If you're not convinced AI is useful, this isn't the right fit

Desirable

• We're looking for a hands-on Lead Engineer to own Platform and Infrastructure at Stream
• You'll work directly with a small, senior team and will be responsible for improving the efficiency, reliability, and cost profile of the systems powering Stream's entire real-time platform
• This is not an architecture-at-a-distance role. You will write code most of the time, lead infrastructure decisions across teams, and shape how we operate at scale. You'll report directly to the CTO
• Why This Role Exists:
• Our platform works — but we're growing fast and there's meaningful work ahead on cost optimization, cloud efficiency, and platform scalability
• Your first 6 months will focus on three areas:
• Cloud cost optimization — identify waste, drive rightsizing, build tooling and guardrails to prevent cost regressions. We want measurable outcomes, not reports
• Platform reliability and scalability — improve observability, define SLOs where they're missing, and harden the systems all of Stream's products depend on
• Architecture and infra evolution — evaluate and drive decisions on Kubernetes adoption, database architecture, and cloud provider strategy (including potential migration work)
• Beyond that, you'll shape long-term platform direction, define engineering standards, and collaborate with product and SDK engineers to keep the developer platform fast to build on
• Core Technologies:
• Go
• Python
• AWS
• GCP
• Kubernetes
• PostgreSQL
• CockroachDB
• Redis
• Terraform
• Prometheus / observability stack