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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Customer Engineer - **Company:** LEADSIMPLE INC. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $74,880.0 - $83,200.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software as a Service, Cloud Computing, Code Review, Programming Tools, Monitoring of Systems, Ruby on Rails, SQL Databases, Working Model 2D, Datadog, ReactJS, Grafana, Bug Reporting, Sentry, Graphql, Cloudwatch, Restful APIs - **Published:** August 8, 2026 - **Apply:** https://app.dover.com/apply/LeadSimple/85afd984-a1f2-471b-8ce4-3476309a83be ## About the Role * 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. ## Description 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. ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [From Doubt to Confidence: How Sentry Uses Verdaccio to Bulletproof SDK Releases](https://www.wearedevelopers.com/videos/739-from-doubt-to-confidence-how-sentry-uses-verdaccio-to-bulletproof-sdk-releases) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [Applying Agile Principles to Incident Management ](https://www.wearedevelopers.com/videos/101-applying-agile-principles-to-incident-management) ## Related Articles - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Is Software Engineering Over-Saturated?](https://www.wearedevelopers.com/magazine/418-is-software-engineering-over-saturated) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)