Enterprise Customer Success Manager

London, England, United Kingdom

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Data Management, Artificial Intelligence, SaaSIndustries

Requirements

Candidates should possess over 3 years of experience in Customer Success, specifically serving enterprises or consulting for large enterprise clients with over 10,000 employees. Experience delivering success for data products or solutions is highly preferred, as is a demonstrated track record in an early-stage company or highly ambiguous environment. Strong project and stakeholder management skills are essential to orchestrate large-scale rollouts and manage through enterprise political dynamics, along with the ability to prioritize complex and competing objectives.

Responsibilities

The Enterprise Customer Success Manager will lead and manage the entire client journey from onboarding to adoption and value realization. This includes partnering with Account Executives to build growth strategies, secure renewals and expansions, and effectively communicate business value to client executives through strategic reviews and insights. Responsibilities also involve partnering with customers to establish clear business goals, timelines, priorities, and success metrics, leveraging product expertise to prescribe best practices, and identifying/developing relationships with client executives. The role requires regularly monitoring account health and adoption to identify opportunities for customers to acquire maximum value and project managing the customer journey using internal and external resources.

Skills

Customer Success Management

Data Reliability

Enterprise Rollouts

Stakeholder Management

Executive Communication

Value Articulation

Account Management

SaaS

Onboarding

Adoption

Project Management

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