Systems Integration Engineer II

Join us in bringing joy to customer experience.

Engineering Operations keeps the systems that plan and track our engineering work running and connected. We are hiring a Systems Integration Engineer to write the code that links those systems, so leaders see the same numbers wherever they look and nobody rebuilds a status report by hand.

• Write the code that connects the systems Engineering Operations depends on. This means Python that calls APIs, parses responses, handles failures, and runs on a schedule or on events. No-code rules and workflow builders are not part of the job.
• Maintain the model that links roadmap items to delivery work, and fix mismatches when they appear, escalating design changes to a senior engineer.
• Ask what decision a request supports before you build it, and raise requests to automate reports nobody acts on.
• Monitor integrations, fix failures, and update the runbook so someone else can fix them next time.
• Build and maintain the automated feeds behind OKR, QBR, and capacity reporting, and cut the manual steps that produce them.
• Support reviews of new tools and connectors by gathering information on cost to run and impact on the people who use them.
• Document how each integration works and what data it moves.
• Help engineering operations team members use the tooling without breaking the data. 

• 2 to 4 years of experience building software that integrates systems, not configuring them.
• Python you can write and debug on your own. You have shipped scripts or services that other people depend on, and you can show the code.
• Working experience with REST APIs. You have handled authentication, pagination, rate limits, and retries, and you are learning how to handle partial failures.
• At least one integration between two systems that weren’t designed to work together, where you mapped mismatched data models.
• Working knowledge of the Jira or Confluence API and permission model, or a comparable work management platform.
• Business sense. You can explain why the company is investing in a system, what the people using it are trying to get done, and when the honest answer is that it isn’t worth building.
• Clear communication. You can explain a data problem to an engineering operations manager and a technical tradeoff to an engineering director.
• A habit of writing down how things work.

• Experience working in an AI-driven development lifecycle (AI-DLC), meaning you use AI coding tools as a collaborator, review what they produce, and can tell good, generated code from code that only looks right.
• Experience with webhooks, queues, or event-driven integrations.
• Experience with a BI or reporting tool.
• Background in an engineering operations or program team.

• Quarterly reports come straight from the systems with no manual rework.
• Monitoring finds integration failures before users do.
• You can name the decision each integration supports, and you retire the ones nobody uses.
• A new engineering operations manager can learn the tooling from the documentation.