Customer Success Engineer - II

Who we are

Key Responsibilities

Operational Support and Issue Ownership

Respond rapidly to customer queries and drive issues to resolution.

Provide timely workarounds and temporary solutions to customers for known issues, ensuring minimal disruption while long-term fixes are being implemented.

Own support tickets end-to-end, ensuring SLA compliance and serving as an escalation point.

Work cross-functionally with Product, Engineering, QE and DevOps to eliminate systemic issues and reduce ticket volume.

Work independently with strong planning and task management skills.

Use data analysis to make actionable, data-driven recommendations.

Technical Investigation, Diagnostics and Root Cause Analysis

Diagnose, reproduce, and debug complex issues across backend services, APIs, data flows, UI behavior, and AI-driven components.

Capture and analyze browser logs, HAR files, network traces, and client-side errors using modern observability tools.

Analyze AI-driven decision paths, validate model outputs, and identify anomaly or drift patterns.

Troubleshoot integrations with external systems, authentication flows, and third-party platforms.

Leverage tools such as Zipy, DataDog, SumoLogic, FullStory, Mixpanel, and Metabase for deep diagnostics.

Identify, diagnose, and surface real-world failure modes in AI workflows, partnering with Product and Engineering to address root causes and deliver more resilient, trustworthy AI experiences.

Provide configuration guidance and technical solutions to resolve customer issues across environments.

Perform structured impact and severity analysis before escalating to Engineering.

Establish repeatable auditing frameworks to bring consistency to investigations.

Knowledge Building, Documentation and Enablement

Build and maintain high-quality runbooks, playbooks, troubleshooting guides, and diagnostic frameworks to reduce repeat incidents and improve response speed.

Convert solved issues into product and documentation improvements for L1, customers, Product, and Engineering.

Drive internal knowledge sharing by conducting deep dive sessions and post-incident walkthroughs.

Communicate solutions clearly to customers and internal teams, translating complex root causes into simple explanations.

Scripting, Tooling and Automation

Write scripts (Bash/Python/SQL) to automate log extraction, data analysis, replication of issues, and health checks.

Create lightweight internal utilities to accelerate diagnostics and reduce manual investigation time.

Contribute to building support automation pipelines and AI-assisted troubleshooting tools.

Requirements

Experience and Background:

4+ years of overall experience and 1-3 years of L2 technical support experience in enterprise SaaS platforms/products (AWS Cloud preferred) in a customer-facing role. 

Proven track record handling technical issues in production environments, supporting distributed systems.

Experience in supporting a multi-tenant SaaS product or platform; familiarity with multi‑tenant architecture, integrations, and configuration management.

Troubleshooting AI-powered Product features

Hands-on experience troubleshooting AI-powered features. Able to distinguish model issues from data, configuration, or system-level failures with precision.

Knowledge of Authentication, Authorization, and Enterprise Integrations

SSO protocols (SAML, OAuth 2.0, OpenID Connect).

Identity and Access Management (IAM) frameworks.

Multi-Factor Authentication (MFA) and Role-Based Access Control (RBAC)

Just-in-time (JIT) provisioning and SCIM (System for Cross-domain Identity Management).

Backend and Systems skills:

Strong understanding of database concepts; proficient in SQL for querying, analysis, and data validation. Experience with relational and NoSQL systems (e.g., PostgreSQL, MySQL) and ability to debug data inconsistencies and performance issues, and hands-on experience in Snowflake.

Experience querying and analyzing logs, identifying patterns (tools like Datadog, Loki, Sumologic, Splunk, Grafana).

Proficient in debugging modern web applications across backend (GoLang/Python) and API layers (REST, GraphQL; gRPC).

Familiarity with AWS services including, Lambada, CloudWatch, S3 as well as experience with Kafka, Kubernetes, and Snowflake.

Frontend and client side skills:

Proficient in debugging React applications.

Capture and analyze client logs (HAR files, network waterfalls, browser dev tools, net-export, wireshark).

Experience with monitoring tools (Zipy, Fullstory) and BI/analytics tools (Mixpanel, Metabase).

Customer Relationship and Soft Skills:

Strong written, verbal communication skills and empathy when interacting with customers.

Ability to build and maintain trusted relationships with customers and internal teams.

Maintains a calm and methodical approach in high pressure situations.

Proactive, detail oriented, ​​thrives in ambiguity, adapts quickly to changing priorities, and manages multiple concurrent issues in a fast paced environment.

Analyzes and prioritizes issues based on SLAs and customer impact.

Good to have:

Familiarity with prompt engineering (helpful for L2 support on AI-powered products).

Understanding of building or configuring AI agents, including workflows, task automation, and basic agent behavior troubleshooting.

Experience in Mobile App troubleshooting on Android and iOS.

Our Culture & Values

Mindtickle is proud to be an Equal Opportunity Employer

All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, national origin, disability, protected veteran status, or any other characteristic protected by law.

Your Right to Work: In compliance with applicable laws, all persons hired will be required to verify identity and eligibility to work in the respective work locations and to complete the required employment eligibility verification document form upon hire.