Technical Customer Engineer

Lead technical escalations from intake to validated resolution: run diagnostics across logs, SQL, APIs, cloud and code; classify root causes; review AI-generated fixes; coordinate engineering responses to meet SLAs; improve runbooks, tooling, and prevention to reduce repeat incidents. The summary above was generated by AI

LeadSimple helps property management companies make their operations simple, scalable, and consistent. We are a remote-first team that moves quickly, cares deeply about quality, and stays close to our customers.

We are redesigning how customer-reported problems move from first report to verified fix. AI will increasingly perform first-pass investigation, reproduce issues, and propose code changes. The hardest cases still require distinctly human judgment: asking the right follow-up questions, earning customer trust, determining whether a report is a defect, configuration issue, or product request, and ensuring the right resolution happens on time.

We are looking for a Senior Technical Customer Engineer to own that boundary. You will lead technical customer conversations, investigate issues across logs, data, APIs, and application code, review AI-generated fixes, coordinate Engineering response against tiered SLAs, and turn recurring escalations into systemic improvements.

What this role is

A senior individual-contributor role at the intersection of customers, Support, Product, and Engineering. It is not a traditional Tier 1 support position and it is not a conventional feature-development role.

What you will own

• Lead discovery and diagnostic calls for escalated customer issues, often alongside Customer Success or Support.
• Turn incomplete or ambiguous reports into clear reproduction steps, timelines, impact statements, and technical evidence.
• Investigate issues using application logs, SQL, APIs, browser tools, cloud and observability platforms, and source code.
• Classify each issue accurately: software defect, customer configuration, data or integration problem, education gap, feature request, or product decision.
• Review AI-generated analyses and pull requests for root-cause alignment, correctness, regression risk, tests, maintainability, and customer impact.
• Improve or ship bounded fixes when appropriate, while coordinating broader changes with the Engineering team.
• Own escalated issues from intake through validated resolution, including clear internal and customer-facing status updates.
• Maintain the operational SLA tracker across customer tier and issue severity, surface breach risk early, and ensure the responsible teams are keeping pace.
• Apply priority and commercial-promise rules defined with Engineering, Product, and Customer leadership; bring evidence when those policies need adjustment.
• Identify recurring failure patterns and improve runbooks, knowledge, diagnostic tooling, tests, support enablement, and AI investigation workflows.
• Use time between escalations to reduce repeat defects and increase the share of issues that can be resolved safely without Engineering intervention.

What success looks like

• Customers feel heard and leave technical calls with credible next steps.
• Engineers receive reproducible, evidence-rich issues rather than vague escalations.
• SLA risks are visible before they become breaches.
• AI-generated diagnoses and fixes become more accurate, testable, and safe to review.
• Repeat incidents decline as findings become durable product, tooling, and support improvements.
• Product questions reach leadership with the customer context and technical evidence needed for sound judgement.

• Five or more years in support engineering, production or application support, product engineering, solutions engineering, or a comparable customer-facing technical role.
• A track record of diagnosing complex production issues in a multi-tenant SaaS application.
• Comfort reading application code, reasoning about proposed changes, and participating in code review; you do not need to be a full-time feature engineer.
• Practical experience with SQL, REST APIs, logs, browser developer tools, cloud infrastructure, and observability systems.
• Excellent customer presence: calm, curious, credible, and professional when the customer is frustrated or the answer is not yet known.
• Clear written communication and the discipline to document evidence, decisions, ownership, and next steps.
• Sound judgment about when to keep investigating, when to escalate, and when a request requires Product rather than Engineering.
• Strong organization across multiple open incidents with different business impact and deadlines.
• Healthy skepticism toward AI output: able to use AI tools productively while independently validating their conclusions and code.

Preferred experience

• Ruby on Rails in production; React, GraphQL, or comparable modern web-application experience.
• AWS and CloudWatch, plus Sentry, Datadog, Grafana, or similar observability platforms.
• Writing or reviewing tests and pull requests for customer-reported defects.
• Operating incident, escalation, severity, or SLA processes.
• Improving support through scripts, internal tools, documentation, automation, or AI-assisted workflows.
• Property management, real estate, or SMB SaaS.

• Remote within the United States.
• Primarily a business-hours role. Rare high-severity incidents may require flexibility outside normal hours; any recurring on-call arrangement will be defined explicitly rather than treated as an unspoken expectation
• Initially, expect a few customer escalation calls per week. As defect volume and diagnostic automation improve, the role should shift toward prevention, tooling, enablement, and product-quality work.
• Leadership sets the commercial promise and priority rules. This role operationalizes them and supplies evidence when the policy needs adjustment.

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