Field guide

    What is support engineering?

    Support engineering is where customer problems meet engineering practice: a discipline that resolves technical issues with code, data, and systems thinking rather than scripts and hand-offs.

    By Mukul Garg, Head of Support Engineering at PubNub · Forbes Technology Council

    What is support engineering?

    Support engineering is the discipline of solving customers' technical problems with engineering methods: reading code and logs, reproducing failures, debugging integrations, and feeding what customers hit back into the product. Where traditional customer support follows scripts and escalates, support engineers resolve technical issues directly and work with product and engineering teams on root causes.

    What does a support engineer do day to day?

    A support engineer troubleshoots customer-reported issues across the stack: reproducing bugs, analyzing logs and metrics, debugging API integrations, and writing runbooks and tooling so the next occurrence resolves faster. Senior support engineers also drive escalation processes, on-call rotations, and the feedback loop that turns recurring tickets into product fixes.

    How is support engineering different from customer support?

    Customer support is organized around service: answering questions, managing accounts, and routing problems to the right team. Support engineering is organized around resolution: the people answering are engineers who can read the code, query the data, and fix or precisely diagnose the problem themselves. Companies with technical products, developer platforms and APIs especially, need the second kind.

    When does a company need a support engineering team?

    As soon as your customers are developers or your product's failures require engineering skills to diagnose. Signs you have outgrown ticket-taking support: escalations routinely land on the product engineering team, time to resolution is driven by hand-offs rather than difficulty, and customers ask questions your support staff cannot technically evaluate.

    How is AI changing support engineering?

    AI is moving support engineering from reactive to proactive: models triage and categorize tickets, surface likely root causes from logs before a human reads them, draft responses for engineer review, and predict failures before customers report them. The teams that benefit pair these systems with strong data quality and keep engineers in the loop for judgment calls.

    How do you measure a support engineering organization?

    Beyond CSAT: time to first meaningful response, time to resolution split by difficulty, escalation rate to product engineering, recurrence rate of known issues, and the volume of tickets eliminated by fixes and documentation. The goal is a flywheel where every resolved issue makes the next one cheaper.

    Building or fixing a support engineering org?

    I have spent 16+ years doing exactly this, most recently building PubNub's global support engineering team from the ground up. If you have questions or need help, reach out.

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