Implementation Engineer, Observe

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact.

We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.

Observe by Snowflake is a high-growth SaaS observability platform built on the Snowflake AI Data Cloud, enabling businesses to troubleshoot modern distributed applications 10x faster. Now, as a core part of Snowflake, we’ve reached a major milestone in the evolution of the Snowflake platform. By bringing AI-powered observability directly into the Snowflake ecosystem, we’ve created the first truly unified platform for telemetry and business data.

We’re looking for an Implementation Engineer to help enterprise customers successfully deploy, configure, and operationalize Observe.

This is a hands‑on, post‑sales technical role focused on delivering strong first outcomes, accelerating time‑to‑value, and establishing a solid foundation for long-term customer success. Implementation Engineers are deeply technical, customer‑facing practitioners who work closely with customer platform, SRE, Dev Ops, and application teams during onboarding and early adoption.

In this role, you’ll translate existing observability architectures (including Open Telemetry‑based pipelines, Splunk, ELK, and other monitoring solutions) into scalable, production‑ready implementations on Observe—using best practices while balancing speed, quality, and customer enablement.

Implementation Engineers focus on initial deployments and time‑to‑value
. They set the technical foundation and tone for the customer relationship by ensuring early success, sound architecture, and a clear path forward. Once customers are live and operational, Observability Engineers and Architects build on this foundation—driving deeper adoption, new use cases, and long‑term value.

• Lead structured implementations for enterprise customers, from kickoff through initial production readiness
• Design and configure telemetry ingestion pipelines across logs, metrics, and traces, including parsing, enrichment, normalization, and routing
• Work hands‑on with Open Telemetry instrumentation and collectors, helping customers adopt modern, vendor‑neutral observability standards
• Migrate and modernize existing observability environments (e.g., Splunk, ELK, cloud‑native monitoring tools) into Observe
• Configure datasets, dashboards, alerts, and core observability assets aligned to customer use cases
• Establish implementation plans, milestones, and success criteria in partnership with customers
• Deliver practical, hands‑on guidance that enables customers to become self‑sufficient on the platform
• Identify technical risks, data quality issues, or architectural gaps early and proactively address them
• Capture implementation patterns, best practices, and reusable assets to improve consistency and scalability across the team
• Collaborate closely with Observability Engineers, Architects, Support, Product, and Engineering to ensure smooth transitions and ongoing success

• 5+ years of experience in customer‑facing technical roles such as implementation engineer, solutions architect, technical consultant, or SRE
• Strong hands‑on experience with observability platforms and telemetry pipelines
• Practical experience with Open Telemetry (instrumentation, collectors, exporters, pipelines)
• Experience working with one or more commercial or open‑source observability solutions (e.g., Splunk, ELK/Elastic, Datadog, New Relic, Dynatrace, Grafana)
• Solid understanding of logs, metrics, and traces, including data modeling and tradeoffs at scale
• Experience working in cloud environments (AWS, GCP, Azure) and…