[Remote] Technical Support Engineer (Customer Facing) at Monte Carlo Data

Americas

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Not SpecifiedCompensation

Junior (1 to 2 years), Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

Data, AI, TechnologyIndustries

Requirements

2+ years of experience in Technical Support Engineering or a related technical customer-facing role (Success Engineering, Customer Engineering, Product Engineering, Sales Engineering, Solutions Architecture)
Comfortable reading logs, querying databases, and investigating root causes using tools like Postman, SQL, or internal debugging systems
Strong understanding of SaaS technologies, foundational programming and coding, and cloud-based architecture
Excellent written and verbal communication skills with a customer-first mindset
Proven ability to work efficiently and independently across technical and non-technical teams, able to prioritize amongst complex and competing objectives
Experience using AI/LLM tools to research or troubleshoot technical issues

Responsibilities

Serve as a frontline technical expert to diagnose and resolve customer issues, ranging from product bugs to configuration questions and usage blockers
Act as the voice of the customer to internal teams, escalating complex issues with detailed context and urgency
Provide timely, empathetic, and accurate responses through email, Slack, or other support channels
Design and refine scalable support processes to improve response times, resolution rates, and CSAT (Customer Satisfaction Score)
Maintain a growing knowledge base to empower customers and reduce ticket volume while championing knowledge-sharing practices within the support team and across the organization
Partner with Engineering and Product teams to automate common workflows and reduce repetitive support burdens
Work closely with Product Managers and Engineers to triage bugs, clarify feature requests, code out fixes, and prioritize customer needs
Collaborate with Go-To-Market teams (Customer Success, Sales, Field Engineering) to support customers’ onboarding, proof-of-value pilots, and post-sales success
Contribute feedback loops that improve product usability, documentation, and onboarding materials
Answer product questions and debug technical issues for both current customers and prospects, taking ownership from initial contact through submitting PRs for fixes
Write and maintain internal and external documentation, and design processes to drive efficiency and collaboration across teams

Skills

Technical Troubleshooting

Customer Support

Debugging

Code Review

PR Submission

Documentation

Process Design

Infrastructure

Architecture

Monte Carlo Data

Provides end-to-end data observability solutions

About Monte Carlo Data

Monte Carlo Data helps businesses ensure the reliability of their data through end-to-end data observability, allowing real-time monitoring of data freshness, volume, schema, and quality. Their platform includes tools for incident detection and resolution, which assist analysts in addressing data quality issues efficiently. By integrating with communication tools like Slack and JIRA, it fits seamlessly into existing data management processes. The goal is to help businesses avoid the costs associated with bad data, making it suitable for data-dependent companies across various industries.

San Francisco, CaliforniaHeadquarters

2019Year Founded

$229.6MTotal Funding

SERIES_DCompany Stage

Data & Analytics, AI & Machine LearningIndustries

201-500Employees

Benefits

Remote Work Options

Risks

Increased competition from Cribl and BigEye may impact market share.

Technical challenges in integrating with vector databases could affect performance.

New Chief Revenue Officer may lead to strategic shifts disrupting operations.

Differentiation

Monte Carlo offers end-to-end data observability for real-time data monitoring.

The platform integrates with tools like Slack, Teams, and JIRA for seamless communication.

Monte Carlo's root cause analysis speeds up data quality incident resolution.

Upsides

Growing demand for data observability tools boosts Monte Carlo's market potential.

Integration with vector databases opens new opportunities in AI model development.

Real-time data monitoring solutions are increasingly sought after by businesses.

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