Technical Support Engineer (East Coast)
Monte Carlo
full-remotemidpermanentother Full remote - San Francisco, US 7 days ago via WTTJ
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Technical SupportData ObservabilityAI AutomationLLMsRunbooksDocumentationSQLPythonCloud TroubleshootingData Stack
About the role
Role Overview
As a Technical Support Engineer at Monte Carlo, you will own the end-to-end customer experience when technical issues arise. You will diagnose and resolve problems across the platform and collaborate across teams to ensure customers are satisfied.
Key Missions
- Diagnose & resolve technical issues across Monte Carlo’s platform, owning cases from triage to resolution.
- Create and maintain documentation, runbooks, and a knowledge base to reduce ticket volume over time.
- Collaborate with Engineering and Product to drive high-priority bug/feature-gap fixes and align on resolutions.
- Contribute to rebuilding the support function using AI tooling and engineering rigor.
Requirements (Profile)
- Builder mentality: write docs proactively, identify broken processes, and propose improvements.
- AI-fluent: understand how AI/ML systems can fail (e.g., model drift) and actively use LLM/AI coding assistants to automate repetitive support work.
- Customer communication: clear, calm, and honest; able to explain complex technical issues to both engineers and executives.
- Data stack fluency: hands-on understanding of modern data tooling such as Snowflake, Databricks, BigQuery, dbt, Airflow, and related systems.
- Technical depth:
- 2+ years in technical support, solutions engineering, or an SRE-adjacent role
- Comfortable with logs, SQL, Postman, and cloud environments (AWS, GCP, Azure)
- Codebase fluency: comfortable working in a Python repo—reading PRs, writing fixes, running tests, and shipping patches.
Nice to Have
- Experience contributing to or testing AI-powered support tooling.
About Monte Carlo
Monte Carlo is a data observability platform company focused on helping teams monitor, debug, and trust their data pipelines. It provides tooling for diagnosing issues across the modern data stack and improving reliability through analytics and automation.
Scraped 6/11/2026