Job Description
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.
Qualifications
- 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.
Working model
- 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.
LeadSimple is an equal opportunity employer. We welcome applicants from all backgrounds and make engagement decisions based on qualifications, merit, and business needs.
Originally posted on Himalayas
