Full-Time
Strategic Sales Engineer
East, US
Posted on 10/31/2025
Monte Carlo Data
501-1,000 employees
End-to-end data observability and incident resolution
Compensation Overview
$120k - $168k/yr
Boston, MA, USA + 1 more
More locations: New York, NY, USA
Remote
Travel up to 25% to client sites; role based in Boston or New York.
 Sales & Solution Engineering (1) |
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- 5+ years of experience in Sales Engineering for a technical product
- Firm understanding of the data ecosystems, which includes cloud data warehousing, data lakes, ETL concepts and workflows, BI solutions, and SQL
- Demonstrated track record in an early-stage company or highly ambiguous environment
- High degree of ownership
- A relentless desire to solve customer problems and chase down answers
- Technical acumen to understand and explain complex architecture
- Ability to manage multiple projects simultaneously, work with different sales people, and support other Sales Engineers where needed
- Strong technical foundation and customer-facing experience
- Ability to connect technical solutions to business value
- Self-starter mentality with comfort navigating ambiguity
- Team-first mindset with a collaborative approach to problem-solving
- Travel to customer sites (up to 25%) to deliver in-person engagements and build strong relationships
- Lead high-impact product demos tailored to customer challenges and priorities
- Drive the discovery process alongside Account Executives to uncover technical requirements and business objectives
- Define technical requirements and use cases, ensuring alignment with the customer's desired outcomes
- Co-develop Business Value Assessments (BVAs) that showcase value, ROI, and strategic fit
- Present BVAs to executive stakeholders, articulating use cases, metrics, and business impact
- Embrace ambiguity – we’re a startup. If the answer doesn’t exist, go find it or build it
- Contribute to a high-performance team culture, setting ambitious goals and holding each other to a high standard
- Build and nurture technical champions in your accounts to drive adoption and expansion
- Partner closely with sales to shape account strategy and accelerate revenue
Monte Carlo Data provides end-to-end data observability to improve data reliability. Its platform continuously monitors real-time data status, including freshness, volume, schema, and quality, so data engineers can verify data integrity. It includes incident detection and resolution tools that alert, investigate, and prevent data issues at scale, and it integrates with Slack, Teams, and JIRA to fit existing workflows. The company aims to help data-dependent organizations avoid bad data by enabling reliable, transparent, and scalable data operations.
Company Size
501-1,000
Company Stage
Series D
Total Funding
$236M
Headquarters
San Francisco, California
Founded
2019
Simplify's Take
What believers are saying
- 73% of enterprises demand AI agent monitoring before deployment, validating massive TAM expansion opportunity.
- Agent Observability launched 2025 addresses hallucination detection and workflow validation for enterprise AI deployments.
- 400+ enterprise customers resolving 1,000 incidents daily demonstrates strong product-market fit and retention.
What critics are saying
- Databricks Unity Catalog embeds observability natively, eliminating third-party tool need for Snowflake/Databricks users.
- Acceldata's full-stack observability with multi-dimensional root cause analysis erodes Monte Carlo's enterprise market share.
- OpenTelemetry standardization commoditizes agent observability as enterprises adopt lock-in-free frameworks natively.
What makes Monte Carlo Data unique
- First end-to-end data and AI observability platform unifying monitoring across data pipelines and AI agents.
- ML-powered anomaly detection requires no configuration, automatically learning behavioral baselines from historical patterns.
- Security-first architecture analyzes metadata without extracting raw data or PII from customer environments.
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Benefits
Remote Work Options
Growth & Insights and Company News
6 month growth
↑ 2%1 year growth
↑ 4%2 year growth
↑ 4%Monte Carlo has launched Agent Observability capabilities providing unified visibility across AI agent lifecycles. The platform monitors four critical pillars: context, performance, behaviour and outputs, addressing a significant gap in enterprise AI deployment. A Monte Carlo survey reveals 73% of enterprises require monitoring and alerting before deploying AI agents, yet 63.4% cite lack of observability as a top deployment barrier. Additionally, 53% of enterprises expect to significantly rebuild or redesign already-deployed AI agent systems. The platform enables teams to detect hallucinations, diagnose performance issues and validate workflow execution. Axios is using Monte Carlo to ensure accuracy in its AI-powered content tagging system, initially built with OpenAI, with plans to expand across 12 additional large language model applications.
G2 recognizes Monte Carlo in 2026 Best Software Awards. The company's data and AI observability platform leverages automation capabilities to efficiently combat data downtime and help enterprises ensure reliability and performance across their systems. Published on Feb. 23, 2026 Rose Velazquez | Feb 23, 2026 Monte Carlo was recently featured in G2's Best Software Awards for 2026, with recognition among the top 50 products for IT infrastructure and IT management. The data and AI observability company earned its placement based on verified user reviews and ratings. The annual ranking identifies top-tier software companies across various categories, emphasizing user satisfaction and product impact. The company's platform is designed to help organizations maintain the health and quality of their data and AI systems. By using machine learning to monitor data pipelines, the technology identifies and alerts teams to data downtime, which refers to periods when data is missing, inaccurate or otherwise broken. This automated approach allows engineers to resolve issues before they affect business operations or decision-making processes. According to Monte Carlo, recent G2 reviews highlight its product's proactive issue detection, time and resource savings for engineering teams, straightforward implementation and immediate value. "We love seeing proof of our mission out in the wild everyday, and are confident that Monte Carlo is helping engineers and data leaders reduce risk, save time and trust their data more than ever before," the company's announcement stated. This article was drafted by a generative AI tool, using information from press releases and company blogs provided by our staff. All content was reviewed by a Built In editor and went through a fact-checking process to ensure accuracy. Errors can be reported to our team at [email protected].
Monte Carlo has entered the agentic AI era, launching Observability Agents on Thursday to help enterprises ensure data quality.
Additionally, to help these and other data and AI reliability initiatives scale and operationalize more effectively, Monte Carlo introduced the Data Operations Dashboard, which gives insights into key operational metrics like number of incidents by data asset owner, time to detection and time to resolution.
Data observability specialist Monte Carlo on Monday unveiled root cause analysis capabilities aimed at making it faster and easier to identify and resolve data quality incidents.