Customer Centric Engineer (EMEA Market)

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This role requires working during EMEA working hours (1:30 pm-10:30 pm IST)

Responsibilities

We are hiring a Customer Centric Engineer to join our Global Customer Centric Engineering team and help deliver Customer satisfaction by solving the toughest technical customer escalations fast and championing a trust strategy throughout the company.

The Customer Engineering team sits at the intersection of Product Engineering and our Success and Support organization, acting as the deepest technical escalation layer for our most critical customer issues. As a Customer Centric Engineer on this team, you will own the end-to-end lifecycle of complex technical escalations — from root cause analysis through code-level fixes — while also driving long-term improvements to how Workato is built, debugged, and supported at scale.

This role requires both the depth of a engineer and the communication range to work fluidly across engineering, support, and customer-facing teams. You will also play a key role in how we adopt AI across our support workflows, tooling, and troubleshooting processes — finding practical ways to apply emerging capabilities to real operational problems.

Own complex technical escalations from our Success and Support teams, applying programming, database, API, and UI expertise to resolve critical customer issues end-to-end.

Identify root causes using systematic troubleshooting techniques and full access to R&D tooling, systems, and codebases.

Leverage AI tools and techniques — including LLM-assisted log analysis, code search, and pattern recognition — to accelerate root cause identification and reduce mean time to resolution.

Dig into source code to propose test cases, reproduce defects, and deliver or recommend fixes to Product Engineering.

Build and maintain internal diagnostic tools and utilities — including AI-assisted tooling — that improve support efficiency and reduce time-to-resolution across Workato teams.

Communicate root cause analyses, workarounds, and resolution timelines to customer-facing teams within defined SLA windows to drive customer satisfaction.

Mentor Support engineers on technical concepts, debugging approaches, and best practices.

Author and maintain internal knowledge base documentation to reduce repeat escalations and build team capability.

Build trusted relationships across Success, Product Engineering, and Infrastructure as a recognized technical expert.

Champion supportability and debuggability initiatives within Product Engineering, ensuring new features are built with observability and diagnosability in mind.

Identify and drive AI-powered process improvements across the escalation lifecycle — from triage and classification to documentation generation and knowledge base maintenance.

Requirements

Qualifications / Experience / Technical Skills

BS/MS in Computer Science, Computer Engineering, Information Systems, or equivalent practical experience.

3+ years of hands-on software engineering experience, including time in a customer-facing or escalation engineering role.

Strong proficiency in Ruby (our core language);

Good understanding of network protocols and distributed systems.

Experience working in SaaS environments and with cloud infrastructure technologies.

Familiarity with both relational databases (e.g., PostgreSQL) and NoSQL data stores.

Soft Skills / Personal Characteristics

Passionate about solving hard technical problems and recommending meaningful improvements to products and processes.

Excellent written and verbal communication skills — equally comfortable writing a sharp bug report, a knowledge base article, or presenting a root cause analysis to senior leadership.

Curious, detail-oriented, and personally invested in doing the work right — you find loose ends and pull on them.

Comfortable working across engineering, support, and customer-facing teams on high-visibility issues where the stakes are real.

Nice to have

Experience building internal tooling or developer productivity infrastructure.

Hands-on experience applying AI or LLMs to engineering workflows — such as automated log triage, anomaly detection, code generation, or intelligent search.

Familiarity with observability platforms, log analysis, or APM tools.

Proficiency in one or more of: Go, Python, or Java — particularly for scripting, automation, or service-level work.

Familiarity with Kafka, Redis, ClickHouse, PostgreSQL, or similar technologies.