Internal Systems Engineer
Keep GI's core systems — CRM, LMS, payments and analytics — working, connected and improving.
- Team
- Technology
- Location
- LATAM
- Work setup
- Remote
- Type
- Full-time
- Level
- Semi-Senior
- Posted
- September 8, 2026
About Growth Institute
Growth Institute is a global leader in executive education for mid-market companies, operating across the U.S., Mexico and international markets. We are in the middle of a shift: from selling courses to being accountable for the outcomes that follow them.
That raises the bar on what our technology has to do. Becoming the operating system for CEOs means enrollment that works the moment someone pays, data that shows whether a leader is actually progressing, and a stack — CRM, LMS, payments, analytics and internal apps — that behaves like one product instead of five. Our teams depend on it every day.
The role
This role exists to keep that stack working, connect it well, and make it better over time.
The mission: support and continuously improve the systems that power GI — ensuring they work reliably, connect effectively, and evolve as the business needs them.
Reports to the Head of Technology. Expected start date: October 1, 2026.
What you will own
Reliable core systems. CRM, LMS, payments, analytics infrastructure and critical internal tools work reliably, with technical issues diagnosed and resolved effectively.
Effective technical support. Responsive support for system, configuration, data and integration issues, escalating when deeper engineering or vendor intervention is required.
Continuous system improvement. Identifying recurring issues, friction and technical limitations, and implementing practical improvements rather than repeatedly solving the same problems manually.
Workflow automation. Automations that reduce repetitive work and improve reliability across business workflows.
Reliable integrations. APIs, webhooks and data flows connecting GI's core platforms — implemented, maintained and troubleshot.
Internal tools and lightweight development. Scripts, utilities, dashboards and lightweight internal applications, built with AI-assisted development and modern coding agents as a standard part of the workflow.
Analytics infrastructure support. Implementation and maintenance of tracking, data pipelines, integrations and analytics infrastructure, in coordination with the owners of analytics and business reporting. This role supports the technical implementation; it does not own analytics strategy, KPIs or business reporting.
System visibility. Logging, monitoring and documentation for critical integrations and workflows, so issues can be understood and diagnosed efficiently.
What we look for
A technical generalist who enjoys figuring out how systems work, connecting them, fixing them when they don't, and continuously making them better.
You don't need to be an expert software engineer. You should be comfortable working with APIs, data, automation platforms and AI-assisted development tools to build practical, reliable solutions.
We don't expect you to design GI's architecture. We do expect that you can take a relatively ambiguous problem inside these systems, investigate it, and carry it through to a solution with good autonomy.
The competencies that matter most:
- Technical problem solving. Investigating unfamiliar issues across multiple systems, following data and integration flows, identifying likely root causes, resolving or escalating appropriately.
- APIs and integrations. Comfort with APIs, webhooks, authentication, JSON and third-party SaaS platforms.
- Automation. Building and maintaining workflows with n8n, Make, Pipedream or equivalent.
- Communication and ownership. Working directly with non-technical stakeholders, owning an issue through resolution, communicating clearly what happened and what is needed.
Nice to have
- AI-assisted development. Building scripts, integrations and lightweight applications with tools like Codex, Claude Code or Cursor. We value reliably shipping working solutions over from-scratch coding proficiency.
- Data and SQL. Querying relational databases, investigating data inconsistencies, working safely with operational data.
- Analytics implementation. Events, properties, tracking plans, data pipelines and analytics integrations — enough to implement and troubleshoot them.
- Systems thinking. Looking beyond the immediate request to dependencies, recurring problems and opportunities for improvement.