Senior Enterprise Customer Success Manager
QuadricSalary not specified
Full Time
Senior (5 to 8 years)
$136,000 – $152,000Compensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Data Management, Artificial Intelligence, SaaSIndustries
Requirements
Candidates must have 5 years of experience in Customer Success serving enterprises or consulting for enterprise-level clients. Strong experience with clients up to 10,000 employees is required, along with a demonstrated track record in an early-stage company or highly ambiguous environment. Project and stakeholder management skills are essential, as is the ability to prioritize complex and competing objectives. Experience delivering success for data products/solutions is highly preferred.
Responsibilities
The Enterprise Customer Success role involves leading and managing the entire client journey from onboarding to adoption and value realization. This includes partnering with Account Executives to build growth strategies, securing renewals and expansions, and effectively communicating business value to client executives. The role requires partnering with customers to establish clear business goals, timelines, priorities, and success metrics, and leveraging product expertise to prescribe best practices for adoption and value enhancement. Additionally, the role involves identifying and developing relationships with client executives, regularly monitoring account health and adoption to help customers acquire maximum value, and project managing the customer journey using internal and external resources as needed.
Skills
Customer Success
Account Management
Client Relationship Management
Data Products
Project Management
Stakeholder Management
Business Value Communication
Strategic Reviews
Best Practices
Enterprise Software
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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