Customer Support Engineer
1 open position
AI is a core part of how we work in Support — you will use it daily across triage, troubleshooting, drafting, and knowledge search. Your edge is knowing when to trust it, when to push back, and when to take over entirely
Communicate with customers at a consistently high standard — every update clear, every expectation set, every resolution confirmed; your written output should be good enough to become a template
Own the support queue with urgency — every ticket acknowledged, categorised, and prioritised within SLA, without hesitation
Resolve complex technical issues end-to-end — configuration failures, integration breakdowns, data inconsistencies, and environment-specific faults that require in-depth investigation
Reproduce customer-reported issues in staging or sandbox environments to confirm behaviour and gather evidence
Dissect issues that others would escalate — trace every problem to its root through logs, SQL queries, and system behaviour before drawing any conclusions
AI is your first call on every investigation — use it to surface patterns, generate hypotheses, and cut through complexity; your job is to direct it, validate it, and close the case
Resolve issues directly — correct data inconsistencies, adjust configuration, apply targeted fixes — you find the answer, you own the fix
Diagnose integration failures between our products and connected third-party systems — data sync issues, file exchange errors, and API misbehaviour across any connected platform
Where a direct fix is not available, you don't wait — engineer a workaround or alternative workflow that keeps the customer operational while the underlying issue is driven to resolution
Drive every open case forward without being asked — own the momentum across customers, internal teams, and engineering until the issue is closed
Reproduce and document issues with precision — environment, configuration state, data conditions, and exact reproduction path; when engineering escalation is needed, your summary is already written: concise, evidence-backed, and actionable without a single follow-up question
For every resolved issue, produce a clear and complete resolution statement — precise enough that anyone could follow it without interpretation; these feed directly into AI-generated knowledge base articles
Author and maintain troubleshooting runbooks for recurring issue patterns — clear trigger conditions, step-by-step actions, and unambiguous resolution confirmation
Identify gaps in product documentation and flag them to the product and engineering teams
Surface recurring issue patterns and systemic failure points to engineering as candidates for permanent fixes or product improvements
Actively look for repetitive ticket types, manual steps, and workflow inefficiencies — and bring forward concrete proposals to automate or eliminate them
Participate in post-incident reviews to capture learnings and update runbooks accordingly
- 6+ years in a technical support or customer success engineering role — SaaS environment is a must, not a preference
- Strong written communication skills — able to produce clear, precise resolution statements, customer-facing updates, and internal escalation notes that anyone can act on without interpretation
- Experience working in AI-augmented support environments — comfortable using AI tooling as part of daily workflow
- Strong analytical thinking and problem-solving skills — able to break down complex issues methodically, cut through noise, and identify root causes without over-complicating the path to resolution
- Proven track record handling escalations and high-severity incidents — calm under pressure, decisive in triage, and clear in communication when it matters most
- Solid understanding of REST APIs — able to independently read request/response payloads, trace integration failures, and diagnose API behaviour without guidance
- Hands-on SQL experience — able to write queries for data validation, investigate data inconsistencies, and support debugging across relational databases
- Comfortable reading .NET/ASP.NET and React code — enough to understand application behaviour and hold informed technical conversations with engineering
- Proficient in log analysis across cloud-hosted environments — able to identify anomalies, trace error origins, and extract meaningful diagnostic information independently
- An eye for patterns and automation opportunities — naturally spots repetitive processes, recurring issue types, and candidates for systematic fixes or AI-assisted resolution
- Experience working with ticketing systems (Zendesk, Jira Service Management, Freshdesk, or equivalent)
- Proven ability to follow and author troubleshooting runbooks without ambiguity
- Highly organised and able to manage competing priorities across a live queue without losing pace or quality
- Collaborative mindset: comfortable working across support, engineering, and product teams in a remote-first environment
- Big Plus: Familiarity with US higher education financial aid processes — Title IV compliance, FAFSA/ISIR processing, packaging and disbursement, or R2T4
- Stands out: Experience supporting SaaS products that integrate with Student Information Systems (e.g. Ellucian Banner, Colleague) or federal education systems
- Stands out: Exposure to EdTech or regulated industry SaaS where compliance and audit trails are central to the product
- Stands out: Experience supporting multi-product or multi-tenant SaaS platforms
- Exposure to cloud environments and an understanding of how cloud-hosted SaaS applications are structured and operated
- Familiarity with observability tooling such as Grafana, Datadog, or similar
- Prior involvement in knowledge base or runbook development