Senior Technical Support Engineer, Supermetrics

• A background in technical or customer support, solutions engineering, or technical operations, especially within a SaaS setting
• Skilled at investigating, diagnosing, and documenting, and able to remain comfortable with uncertainty until the evidence becomes clear
• Comfortable reading and making small changes to code, working with version control, and understanding how APIs and data pipelines fit together
• An agentic mindset: confidence directing an AI coding agent toward a fix, paired with discipline about testing its output rather than taking it on faith
• Genuine curiosity about how data moves and breaks, with motivation to build deep familiarity with Supermetrics' connectors and integrations over time
• Clear and effective communication in writing and speaking, making technical details understandable for customers and engineering stakeholders
• A structured, methodical approach to managing multiple open cases and customer expectations through uncertainty

Desirable

• Supermetrics is looking for a Senior Technical Support Engineer to own our most complex customer relationships
• This isn't a software engineering role, resolving tickets and owning the technical relationship is the core of the job, but you're expected to arrive already comfortable directing agentic AI tools, such as AI coding assistants built into your editor or terminal, to diagnose and fix issues faster, not treat that as a stretch skill to pick up later
• You'll move fluidly between talking a customer through a connector failure and using an AI coding agent to trace a schema mismatch to its root cause, and as your product depth grows, you'll increasingly turn bug reports into proposed fixes rather than just well-documented tickets for someone else to pick up
• As AI handles more of the day-to-day questions, this role becomes about the cases that need real technical judgment, and using AI to solve problems that used to require an engineering handoff
• Own resolution of technical and product queries, from connector issues and pipeline failures to authentication errors and schema mismatches, with accuracy, clarity, and pace
• Act as a trusted technical point of contact, diagnosing root causes systematically and explaining findings in accessible, non-reductive language
• Flag signals of broader product or account risk, such as recurring failures, integration instability, or data quality concerns, with context and recommended next steps
• Partner with Product, Engineering, and Customer Success on escalated cases, handing off with well-structured bug reports, clear reproduction steps, and context that makes the next person's job easier
• Maintain the technical knowledge base: known issues, FAQs, and resolution guides that reduce repeat contact
• Use agentic AI coding tools to navigate unfamiliar code, write small scripts, and investigate failures down to root cause, then propose a draft fix for Engineering to review where you have the access and confidence to do so
• Test and validate AI-generated output before proposing it as a fix, the same way you'd validate any other diagnosis
• Serve as a technical AI assistant specialist by outlining diagnostic workflows, selecting knowledge sources, and checking AI-generated responses for correctness
• Use patterns in query volume to improve AI deflection coverage, so human engineers focus where deep diagnostic reasoning matters most
• Help define and safeguard the boundary between what AI handles autonomously and what needs human intervention