Deployment Engineer

Jibe Ventures; San Francisco (CA); Hybrid; USD 110,000 - 170,000; Full time

Job summary

groundcover is changing how engineering teams understand and troubleshoot cloud-native applications. Our observability platform unifies infrastructure, application, and service visibility using eBPF and a deployment model that runs in the customer’s own cloud.

We’re growing our Customer Success team with a Deployment Engineer who combines hands-on engineering with practical project management. You’ll lead migrations from kickoff through production readiness, help customers deploy groundcover,

Qualifications

• 4+ years in implementation, professional services, deployment engineering, solutions engineering, or a comparable technical role.
• Hands-on experience with Kubernetes, containers, and at least one major cloud platform: AWS, Azure, or GCP.
• Experience with observability platforms and telemetry pipelines, including tools such as Datadog, Grafana, Prometheus, or OpenTelemetry.
• Ability to write and troubleshoot scripts, work with APIs, and use automation to simplify repetitive technical tasks.
• Strong project coordination and customer communication skills, with the ability to manage parallel engagements and explain technical tradeoffs clearly.
• Experience using AI tools in technical workflows, with an interest in building reusable skills and validating results.
• Experience migrating observability platforms or delivering production cloud native infrastructure rollouts.

Responsibilities

• Lead implementation and migration projects from kickoff through production readiness, customer acceptance, and handoff to the Technical Account Manager.
• Build practical project plans with clear scope, milestones, owners, and dependencies. Keep customers and internal teams aligned on progress, risks, and next steps.
• Work directly with customer engineering teams to deploy groundcover, configure integrations, and validate logs, metrics, and traces across their environments.
• Help customers migrate from platforms such as Datadog, Coralogix, New Relic, and Grafana's open-source and commercial offerings, including dashboards, alerts, queries, and operational workflows.
• Use and improve migration tools, scripts, and reusable AI skills and workflows. Validate their outputs to increase delivery speed, accuracy, and repeatability.
• Coordinate migration testing and cutover plans, helping customers verify coverage and resolve gaps before completing the transition.
• Deliver practical enablement and document configurations, decisions, and remaining actions so customers and TAMs can build on a successful implementation.
• Partner with Solution Engineers, TAMs, Support, Product, and Engineering to remove blockers and turn recurring implementation challenges into product and tooling improvements.

Skills

Kubernetes

Containers

Cloud platforms

Observability tools

Automation scripting

Project coordination

Customer communication

AI workflow tools

Tools

Datadog

Grafana

Prometheus

OpenTelemetry

Job description

Deployment Engineer

• Customer Success
• San Francisco

Description

groundcover is changing how engineering teams understand and troubleshoot cloud-native applications. Our observability platform brings together infrastructure, application, and service visibility using eBPF and a deployment model that runs in the customer's own cloud. We help teams investigate problems faster, reduce operational complexity, and scale observability without compromising visibility or control.

We're growing our Customer Success team with a Deployment Engineer who combines hands-on engineering with practical project management. You'll work closely with customers during the first weeks of their engagement, helping them deploy groundcover and migrate from existing observability platforms. From the first planning session to production, you'll turn complex transitions into clear, manageable projects and help improve the tools, automation, and AI skills that make each implementation faster and more reliable.

Location: San Francisco, CA or Boston, MA (Hybrid, 3 days/week in office)

What You'll Do

• Lead implementation and migration projects from kickoff through production readiness, customer acceptance, and handoff to the Technical Account Manager.
• Build practical project plans with clear scope, milestones, owners, and dependencies. Keep customers and internal teams aligned on progress, risks, and next steps.
• Work directly with customer engineering teams to deploy groundcover, configure integrations, and validate logs, metrics, and traces across their environments.
• Help customers migrate from platforms such as Datadog, Coralogix, New Relic, and Grafana's open-source and commercial offerings, including dashboards, alerts, queries, and operational workflows.
• Use and improve migration tools, scripts, and reusable AI skills and workflows. Validate their outputs to increase delivery speed, accuracy, and repeatability.
• Coordinate migration testing and cutover plans, helping customers verify coverage and resolve gaps before completing the transition.
• Deliver practical enablement and document configurations, decisions, and remaining actions so customers and TAMs can build on a successful implementation.
• Partner with Solution Engineers, TAMs, Support, Product, and Engineering to remove blockers and turn recurring implementation challenges into product and tooling improvements.

Requirements

• 4+ years in implementation, professional services, deployment engineering, solutions engineering, or a comparable technical role- Must
• Hands-on experience with Kubernetes, containers, and at least one major cloud platform: AWS, Azure, or GCP - Must
• Experience with observability platforms and telemetry pipelines, including tools such as Datadog, Grafana, Prometheus, or OpenTelemetry - Must
• Ability to write and troubleshoot scripts, work with APIs, and use automation to simplify repetitive technical tasks.
• Strong project coordination and customer communication skills, with the ability to manage parallel engagements and explain technical tradeoffs clearly.
• Experience using AI tools in technical workflows, with an interest in building reusable skills and validating results.
• experience migrating observability platforms or delivering production cloud native infrastructure rollouts.