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Investigate technical issues reported by enterprise customers - analyse symptoms, form hypotheses, follow the evidence to the root cause
Use observability tooling (Datadog logs, traces, metrics, dashboards) to diagnose issues across distributed services
Read code across multiple repositories to verify hypotheses and trace request flows
Query databases when needed to confirm system state and reproduce issues
Manage tickets through their full lifecycle, from intake to resolution, across the support → engineering escalation chain
Communicate proactively and professionally with enterprise customers: structured updates, clear next steps, calibrated expectation management
Partner with engineering teams when escalation is needed - frame issues clearly, provide reproducible evidence, advocate for customer impact
Document findings, contribute to internal knowledge bases, and surface recurring patterns to drive systemic improvements
Participate in rotational on-call coverage for high-severity incidents (evenings, weekends), supporting our enterprise customers when issues cannot wait until business hours
Take ownership of a technical domain within the platform and act as the reference point for the team in that area
Develop and maintain investigation playbooks, runbooks, escalation paths, and knowledge base content the team relies on
Analyse recurring failure patterns to drive platform improvements, and advocate for customer-impacting fixes by framing systemic risk and business impact to product and platform teams
Apply AI tooling to our daily work - evaluate tools and agents, identify where they add leverage, and share what works with the team
Support the onboarding of new team members and help raise the standard for technical investigation and customer communication
5+ Years: Hands-on technical support or systems engineering in a complex B2B SaaS or enterprise environment
Proven experience supporting enterprise (B2B) customers, ideally in a SaaS or cloud-based product environment
Experience improving how a support team works: documentation, playbooks, processes, quality standards
Experience supporting or mentoring less experienced colleagues
Comfortable reading server-side code in at least one language to trace logic and verify code paths
Hands-on experience with observability and central logging tooling - Datadog preferred; familiarity with central logging platforms (Kibana / OpenSearch / Elasticsearch or similar) is a strong plus
Comfortable searching logs, reading distributed traces, querying metrics
Solid SQL skills - able to query relational databases to verify system state, reproduce issues, and understand data models
Working knowledge of distributed systems concepts - async vs. synchronous communication, queues, retries, idempotency, eventual consistency
Comfort with HTTP and API debugging - curl, response headers, status codes, basic DNS, REST and webhook flows
Familiarity with cloud-based, multi-tenant SaaS architectures - understanding tenant isolation and configuration delivery is a strong advantage
Hands-on with AI tooling - you already use AI in your daily work and have gone beyond typing into a chat window: built an agent, automated a workflow, written something against an API, or experimented seriously in your own time
Nice to Have
Experience reading PHP - our platform is largely PHP, so this will help you get up to speed faster
Experience in e-commerce or digital commerce platforms (storefront, checkout, payments, catalog, order management)
Prior incident management or major-incident response experience.
Familiarity with PSP integrations, OMS, PIM, or search platforms
Employment type
Full-time
Work arrangement
Yes
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