Solution Engineer

Logz.io provides an intelligent and scalable machine data analytics platform built on ELK and Grafana. Designed for monitoring modern applications, Logz.io combines cloud-native simplicity and scalability with…

Location: Tel Aviv
Company size: 100–500
Posted: 2mo ago
Via: Lever

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Solution Engineer - Logz

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Job Title: Solution Engineer
Job Location: Tel Aviv
Job Listing URL: https://jobs.lever.co/logz/2de3af90-c578-4984-af54-f31f7690d765
Job Description: The solution engineer is a highly technical, core member of the sales and customer success organization, aligning our cutting-edge observability and agentic AI technologies with our enterprise customers' architectures. We are customer-obsessed, highly technical, and foster a team culture that is entrepreneurial, professional, and fun. In this role, you will act as a trusted technical advisor, owning the long-term architectural relationship and deep-tech integrations for our top-tier enterprise logos.

Location: Hybrid (3 days from the office)

Workweek: Monday–Friday

Responsibilities

Architectural Ownership: Manage all technical touchpoints for strategic customers, driving deep-tech onboarding, advanced enablement, architectural workshops, and complex issue resolution.

Value Optimization: Analyze customer data pipelines and infrastructures to develop and execute technical strategies that maximize observability value and ROI.

Technical Stakeholder Management: Build deep technical peer relationships with DevOps, SRE, and Engineering leaders, diagnosing complex infrastructure challenges and designing production-ready solutions.

POCs & Technical Expansion: Execute rigorous Proofs of Concepts (POCs) and architect complex expansions. Identify technical opportunities for upselling or cross-selling advanced agentic AI and observability modules to drive incremental revenue growth.

Advanced Deployment: Ensure a frictionless, deeply integrated onboarding process for enterprise customers, managing complex log/metric/trace routing and agentic AI deployments.

Adoption Analytics: Monitor deep-tech adoption patterns and infrastructure usage, proactively debugging systemic bottlenecks, scaling friction, or implementation barriers.

What Your Day-to-Day Will Look Like

Manage customer technology interactions: tech value proposition, POCs, onboarding, integration, enablement, implementation assistance, and AI use case adoption.

Automating Integration Workflows: Design and implement automated, scalable onboarding pipelines and configuration workflows to guide enterprise environments through seamless setup.

Proactive Architecture Reviews: Conduct deep-dive engineering check-ins around critical milestones (e.g., post-onboarding, scaling peaks, or architecture reviews) to ensure optimal system performance.

Telemetry & Infrastructure Audits: Analyze customer data volume, telemetry pipeline health, and behavioral patterns to catch underperforming configurations or indexing issues before they impact the customer.

Feedback Integration: Gather technical feedback, feature gaps, and product limitations from engineering stakeholders to advocate for and collaborate with internal product and R&D teams.

Technical Upsell Engineering: Analyze data ingestion and infrastructure usage to engineer custom product/feature recommendations tailored to the customer's specific technical scale with a focus on agentic workflows.

Requirements

Experience: 3+ years of experience as a solution engineer, technical account manager (TAM), or DevOps/SRE engineer with strong customer-facing capabilities.

Domain Expertise: Vast hands-on technical domain experience in SaaS infrastructure, cloud architectures (AWS/Azure/GCP), observability solutions (log management, APM, tracing, and OpenTelemetry), and Agentic Solutions

Technical Communication: Elite customer-facing engineering skills—expert at training engineering teams, whiteboarding complex cloud topologies, resolving technical roadblocks, and translating deep-tech architectures into clear business value.

Language Skills: Exceptional written and verbal professional communication skills in English.

Mindset: Driven, self-starter technical professional with a passion for problem-solving and a desire to constantly push boundaries in AI and observability tech.

Education: BA/BSc degree in computer science, software engineering, or equivalent deep technical experience.