[Remote] Product Manager, Data Operations at Monte Carlo Data

Americas

$160,000 – $180,000Compensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Data, AIIndustries

Requirements

2–4 years of product management or adjacent experience (e.g. software engineering, sales engineering, or analyst background)
Experience working on technical SaaS products or a strong interest in data/AI infrastructure
Care deeply about your work and the outcomes you drive for customers
A bias toward action - you make decisions quickly, iterate, and value progress over perfection
Ability to excel in ambiguity and drive projects forward
Curiosity and a learning mindset - you’re excited to work with senior PMs and engineers to grow your product craft
Strong communication and collaboration skills; you value working across disciplines and functions

Responsibilities

Collaborate with engineers and designers to define and deliver product capabilities that help customers triage alerts and measure data and AI observability initiatives
Work with customers to understand how they operate their data and AI systems - and translate their pain points into product improvements
Use product analytics and customer feedback to prioritize and iterate on what drives impact
Measure your impact by user engagement with alerts and reporting, and how effectively we help them triage and resolve issues

Skills

Key technologies and capabilities for this role

Product ManagementData ObservabilityAICustomer InterviewsProduct LifecycleAdoption MetricsData EngineeringData Governance

Questions & Answers

Common questions about this position

What is the salary range for the Product Manager, Data Operations role?

The salary range is $160K - $180K.

Is this position remote?

Yes, the position is remote.

What experience is required for this role?

Candidates need 2–4 years of product management or adjacent experience such as software engineering, sales engineering, or analyst background, plus experience with technical SaaS products or strong interest in data/AI infrastructure.

What is the company culture like at Monte Carlo?

The team prioritizes making customers extremely happy through the product, owns impact end-to-end by driving adoption, thrives in a high-growth environment, and focuses on innovation for data+AI observability with a bias toward action and customer feedback.

What makes a strong candidate for this Product Manager role?

Strong candidates have 2–4 years in product management or related fields, experience with technical SaaS or data/AI interest, care deeply about customer outcomes, and demonstrate a bias toward action with quick decision-making and iteration.

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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