Automation Software Engineer
Department: Automation
Level: Entry to Mid-Level
About the Role
The Automation Software Engineer builds, implements, and maintains automation solutions for our clients and our internal operations. This role bridges software development and operational support: you will write and configure automations, validate and deploy them to production, and keep them running reliably over time.
You'll work alongside a Systems Automation Engineer who handles platform configuration and operational support. What sets this role apart is code: you are the engineer the team turns to when an automation needs custom development, scripting, or integration work beyond what the platforms provide out of the box.
This role requires hands-on coding experience. Junior-level is acceptable — we develop our engineers. MSP or industry experience is not required; we will train the right candidate on the managed services world.
What You'll Do
- Design, build, and configure automation solutions to meet business and technical requirements
- Test and validate solutions before production deployment, following department standards
- Monitor production automations, investigate issues, and resolve incidents promptly
- Diagnose root causes of operational problems and implement sustainable fixes rather than temporary workarounds
- Build and extend AI-assisted automations using our platform stack (Pia, Thread, ElectroNeek), working within governance standards set by SOC/Compliance
- Identify automation opportunities across client environments and service delivery
- Maintain documentation, runbooks, and support procedures so any engineer can support your work
- Communicate proactively with technical teams and business stakeholders, flagging risks early and following through on commitments
What We're Looking For
Required:
- Hands-on experience with at least one scripting or programming language (e.g., PowerShell, Python, C#, JavaScript) — you have written and modified working code and are motivated to develop further. Junior-level experience is acceptable; demonstrated hands-on experience is required
- Exposure to connecting systems together — APIs, webhooks, integrations, or data transfers — through work, projects, or self-directed learning
- Sound troubleshooting instincts: the ability to isolate a problem, form a hypothesis, and test it methodically
- Clear written and verbal communication with both technical and non-technical audiences
- Accountability: you meet commitments, surface risks early, and follow established processes
Nice to Have:
- Experience with our stack or comparable tools: ConnectWise Manage (PSA), Datto RMM, Pia, Thread, ElectroNeek — or equivalents like Autotask, NinjaOne, Rewst, n8n, or Power Automate
- Experience with AI-assisted or agentic automation tools in a service delivery environment
- Exposure to software engineering practices: version control, code review, testing
- Experience supporting or managing production systems (monitoring, incident response, change management)
- Familiarity with the MSP industry or comparable multi-client service operations
Competencies
1. Automation Development & Delivery (Delivery)
Description
Designs, builds, configures, and implements automation solutions that improve operational efficiency while meeting business and technical requirements.
Expectations
Develops and implements solutions that follow established engineering and department standards. Work is validated before production, produces maintainable results, and requires minimal rework after deployment.
Metrics
Deployment success rate · Code review acceptance rate · Rework rate · On-time completion of assigned work
2. Quality & Reliability (Reliability)
Description
Ensures automation solutions perform consistently through testing, validation, proactive monitoring, and timely defect resolution.
Expectations
Solutions are appropriately tested before deployment and monitored proactively in production. Issues are addressed promptly, root causes are documented, and recurring failures are minimized through corrective action.
Metrics
Post-release defect rate · Automation availability/uptime · Mean time to resolution (MTTR) · Repeat incident rate
3. Technical Problem Solving (Problem Solving)
Description
Diagnoses operational and software issues, identifies root causes, and implements effective, sustainable solutions to maintain system performance.
Expectations
Troubleshoots issues independently when appropriate, escalates effectively when necessary, and focuses on preventing repeat failures rather than applying temporary fixes.
Metrics
Mean time to diagnose issues · Root cause documentation rate · Repeat incident rate · Resolution within SLA
4. Documentation & Knowledge Management (Documentation)
Description
Maintains accurate technical documentation, runbooks, and operational procedures that enable efficient support, maintenance, and knowledge transfer.
Expectations
Documentation is complete, current, and updated alongside changes. Knowledge is stored via standardized methods, is sufficient for another engineer to support the solution, and reduces single points of failure.
Metrics
Documentation coverage · Documentation update timeliness · Runbook completeness · Support issues caused by documentation gaps
5. Ownership & Professional Collaboration (Ownership)
Description
Demonstrates accountability for assigned work while communicating effectively with technical teams, operational teams, and business stakeholders.
Expectations
Meets commitments, communicates risks proactively, follows established processes, manages expectations, and works collaboratively to deliver successful outcomes.
Metrics
Commitment reliability · On-time delivery of assigned work · Stakeholder feedback · Team collaboration feedback
6. AI-Assisted Automation (AI Enablement)
Description
Leverages AI-assisted and agentic automation tools (e.g., Pia, Thread, ElectroNeek) to build, extend, and improve automation solutions while operating within established governance and compliance standards.
Expectations
Uses AI-assisted tooling to increase automation coverage and delivery speed without sacrificing quality. Validates AI-generated outputs and agent behavior before production use. Adheres to AI governance standards set by SOC/Compliance and surfaces automation opportunities to IT leadership rather than deploying outside established processes.
Metrics
AI-assisted automation adoption rate · Validation findings on AI-generated work · Governance/compliance adherence · Automation opportunities surfaced and accepted into the pipeline.