Agave Technical Support Engineer to own the support queue and resolve customer issues for Agave's AI platform. Collaborate with Solutions Engineers, PMs, and Engineers to define support processes and improve documentation.
Responsibilities
Own the support queue and drive tickets to resolution
Triage, diagnose, and resolve customer issues
Improve documentation and playbooks
Surface patterns to shape product roadmap
Collaborate with Solutions Engineers, PMs, and Engineers
Define support processes, SLAs, and tooling
Qualification
2-8 years of experienceFamiliarity with APIsExposure to constructionCustomer-centricEnergized by peopleStrong communicatorTechnically curiousProblem solver
Required
2-8 years of experience, ideally in b2b SaaS.
2+ years in a customer-facing role (support, CSM, solutions, or implementation) where you helped customers resolve nuanced, non-scripted issues.
Experience owning a queue of tickets or cases and driving them to resolution, not just handing them off.
Preferred
Experience supporting a technical product (integrations, data pipelines, accounting, or similar).
Familiarity with APIs, data formats like JSON, and basic database concepts (tables, simple SQL queries).
Exposure to construction, accounting, or ERP/accounting systems.
Customer-centric. You’re good with people, calm under pressure, and able to build trust quickly with busy customers (finance and operations leaders at construction companies).
Energized by people: you will work 1:1 with customers via email, in-app chat, and on Zoom; you are energized by building relationships and helping customers accomplish their goals with Agave.
Strong communicator. You write crisp, clear emails and tickets. You can explain complex issues in simple language and know when to ask follow-up questions vs. guess.
Technically curious. You’re comfortable learning how APIs, integrations, and databases work at a conceptual level. You don’t need to be an engineer, but you don’t shy away from JSON payloads, logs, or configs.
Problem solver. You like digging into ambiguous problems, breaking them down, and testing hypotheses instead of just trying random fixes.
