Manufacturing Systems Support Engineer- Night Shift

Slate Auto•Warsaw, IN

8d•Onsite

About The Position

This is an entry-level role for someone who is technically grounded, methodically under pressure, and comfortable working on the factory floor alongside operators and supervisors as well as with software engineers and IT. You will diagnose and resolve issues across MES, WMS, CMMS, and their integrations — reading logs, querying data, following a hypothesis, and escalating with precision when you reach the limits of your access. If you are energized by troubleshooting real problems in a fast-moving environment and want to build deep expertise in how manufacturing software systems work, this is where that career starts.

Requirements

0–3 years of experience in IT support, helpdesk, technical operations, or a related role; exposure to manufacturing or industrial systems is a bonus but not expected.
Able to read and interpret application logs to distinguish a user error from a misconfiguration from a software defect — the error message is the starting point, not the answer.
Basic SQL: able to write straightforward queries to look up records, verify data integrity, cross-reference tables, and support incident investigation without needing a developer to pull the data for you.
Working understanding of how web-based software systems operate — browsers, REST APIs, databases, and how requests flow between them — sufficient to trace where in a stack something went wrong.
Experience using an ITSM ticketing system such as ServiceNow, Jira Service Management, Zendesk, or similar to manage and document support work.
Comfortable working in both Windows and Linux environments at an operator and configuration level.
Methodical troubleshooter: you form a hypothesis, test it, confirm or rule it out, and move to the next one. You do not randomly change things hoping the problem goes away.
Clear communicator under pressure: when a production line is down, you can tell a supervisor what you know, what you are doing, and what you do not know yet — all in plain language, without adding to the panic.
Owns the ticket to closure: you do not close an issue until you have confirmed it is resolved, not just until the user stopped responding. Verified resolution is the bar.
Curious about root causes: “It’s working now” is not an acceptable end state when you do not know why it failed. You document what you found even when you cannot yet explain it, because that note prevents the next incident.
Comfortable on the floor: manufacturing environments are active and sometimes loud. You are willing to walk out to a station and observe a problem firsthand when context would help you diagnose it faster.
Keeps learning: manufacturing systems are broad and deep. You do not need to know everything on day one, but you should want to understand something new every week.

Nice To Haves

Exposure to manufacturing or industrial systems is a bonus but not expected.
Familiarity with monitoring or observability dashboards such as Grafana, Zabbix or Datadog is a plus.
Basic scripting ability in Python, Bash, or similar — not required, but valued and a strong indicator of the curiosity this role rewards.
Manufacturing or industrial environment experience is welcomed but not required for a candidate with strong fundamentals and genuine curiosity about how things get built.

Responsibilities

Serve as the first point of contact for manufacturing system issues — triage incoming requests from operators, supervisors, and quality engineers across MES, WMS, and CMMS.
Diagnose software issues by reading application logs, querying databases, and tracing data flows to isolate whether a problem is a user error, a configuration issue, a data integrity problem, or a software defect.
Follow established troubleshooting runbooks to resolve known issue patterns; document any gaps or improvements you discover so the knowledge base stays accurate and genuinely useful.
Escalate to the software engineering team when an issue requires a code fix — write precise escalation tickets that include what you observed, what you ruled out, and what data supports your conclusion.
Monitor system health dashboards and act on anomalies before they become production-stopping events; know what normal looks like so you can recognize when something isn’t.
Analyze incident data to identify recurring patterns and high-frequency issue types — surface these trends to the engineering team with supporting data to drive permanent fixes rather than repeated workarounds.
Manage user administration tasks including account setup, access provisioning, and permission changes across manufacturing system platforms.
Support software release activities — execute test scripts, validate deployments against expected behavior, and flag regressions before they reach the production floor.
Walk the floor when the problem requires it — some issues only make sense when you see them in context at the station where they are happening.