Technical Support Engineer, Monte Carlo
East Coast
$100-130k
+ Equity
More information about location
Data observability platform
Be an early applicant
Data observability platform
501-1000 employees
Be an early applicant
$100-130k
+ Equity
More information about location
501-1000 employees
Company mission
To accelerate the world’s adoption of data by reducing data downtime.
Role
Who you are
- Technical Depth — 2+ years in a technical support, solutions engineering, or SRE-adjacent role. Comfortable reading logs, writing SQL, using Postman, and navigating cloud environments (AWS, GCP, Azure)
- Codebase Fluency — Comfortable finding your way around a Python repo: reading PRs, writing fixes, running tests. You don't need to be a full-stack engineer, but you should be able to ship a patch
- Data Stack Fluency — You know the modern data stack well enough to hold your own: Snowflake, Databricks, BigQuery, dbt, Airflow, or similar. Customers run complex pipelines and you'll need to understand what's happening
- AI-Fluent — You understand how AI agents and ML-driven systems can fail. You're not intimidated by probabilistic outputs, model drift, or "it worked yesterday." You've used AI coding assistants and LLM tools actively in your workflow — to write runbooks, debug faster, draft responses, or prototype automations — not just experimented once. Bonus: you've contributed to or tested AI-powered support tooling
- Customer Communication — Clear, calm, and honest under pressure. You can explain something technically complex to a data engineer and to a VP of Data in the same ticket
- Builder Mentality — You write docs without being asked. You notice when a process is broken and propose a fix. You'd rather use AI to automate a repetitive support task than do it manually three more times — and you have examples of doing exactly that
What the job involves
- Monte Carlo is hiring Technical Support Engineers to own the end-to-end customer experience when things go wrong — from the first Slack message to closing the loop with Engineering. This is not a ticket-routing function. You'll dig into customer data stacks, reproduce issues in complex environments, write internal runbooks, and ship fixes to production as a regular part of the job — not an exception
- You'll be joining at a moment when the support function is being rebuilt with AI tooling and proper engineering rigor — which means you'll have real input into how this team operates
- Diagnose and resolve technical issues across Monte Carlo's platform — data pipelines, monitors, alerts, integrations, and agent observability features — using logs, SQL, APIs, and whatever it takes
- Own issues end-to-end: triage, reproduce, escalate to Engineering when needed, validate fixes, and close the loop with customers
- Build and maintain documentation, runbooks, and a knowledge base that actually reduces ticket volume over time
- Work alongside the team building AI-powered support tooling — contribute to prompt design, test coverage, and escalation logic for the bot handling tier-1 setup and FAQ
- Partner with Engineering and Product on bugs and feature gaps — you're the person who can say "I've seen this five times this week" with receipts
- Drive high-priority customer issues over the line — own the coordination across Engineering, CS, and the customer, keep everyone aligned, and don't let urgency get lost in someone else's backlog
- Collaborate with Customer Success, Sales, and Field Engineering to ensure customer issues don't fall into gaps between teams
- Use AI to surface patterns across cases and bring them to Engineering and Product with data — then build or contribute to the automation that handles those patterns so the team can focus on the complex ones
Our take
Dozens of billion-dollar companies have emerged with a shared focus on easing the burden on engineering teams constantly battling to guarantee reliability in their products. For example, New Relic revolutionised application observability. In the same way, Monte Carlo is tackling data observability.
The service pulls information from metadata log metrics to help teams understand their data health, and if something breaks, the product will send proactive notifications. The product is an off-the-shelf SaaS offering that customers can integrate and customise over time.
The company has won some major clients including big tech companies like Affirm and Intuit, but also JetBlue, PepsiCo, CNN, The New York Times, Asics, Rivian and others from many sectors. Its goal is to help companies reduce data downtime and the time data engineers must spend on tedious data quality tasks.
'Data Observability’ became a point of furore in venture capital in 2022. For example, in the span of one week, just three companies raised more than $400 million between them. Staying at the forefront of this industry is an interesting challenge for Monte Carlo, and it met it in 2024 with product enhancements that include new GenAI capabilities for its platform.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
32% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
May 2022
$135m
SERIES D
Aug 2021
$60m
SERIES C
Total funding: $236m
Company HQ
Dolores Heights, San Francisco, CA
Leadership
Barr Moses
(Co-Founder & CEO)Previously VP of Customer Success Operations for Gainsight. Originally trained as a Management Consultant with Bain & Company. Also attended Stanford where they studied Mathematics and Computer Science.
Lior Gavish
(Co-Founder & CTO)Previously SVP of Engineering for another tech startup, Barracuda. Also previously worked for PayPal and holds an MBA from Stanford University.
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