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Full-Time

Engineering Manager

Posted on 8/22/2024

Monte Carlo Data

Monte Carlo Data

201-500 employees

Provides end-to-end data observability solutions

Data & Analytics
Enterprise Software
Financial Services

Compensation Overview

$240k - $245kAnnually

Senior, Expert

San Francisco, CA, USA

Category
Data Engineering Management
Engineering Management
Software Development Management
Required Skills
Python
Data Science
Requirements
  • Bachelor’s degree or foreign degree equivalent in Software Engineering, Computer Science, Cognitive Science, Data Science, or related
  • 60 months of experience in an occupation related to Data Science and Engineering.
  • 60 months in/with the following: Machine Learning, Statistical Analysis, Data Modeling, Python Software Development, Data Pipeline Engineering, and Software Team Management, including architecting, planning, scheduling, recruiting, and team building.
Responsibilities
  • Manage and lead data science projects, prioritize the anomaly detection team’s workload, and guide the entire life cycle of projects from research and engineering to production.
  • Architect and implement the company’s MLOps (Machine Learning Operations) infrastructure, including ML (Machine Learning) pipelines, feature engineering, feature store, and model monitoring for productizing machine learning models.
  • Plan and manage engineering projects from conception to completion, ensuring that they are completed on time, within budget, and to the required quality standards.
  • Review technical designs, specifications, and engineering drawings to ensure compliance with project requirements, standards, and codes.
  • Work with cross-functional stakeholders to unblock projects and ensure information flow.
  • Ensure customer issues are resolved fully and timely.
  • Hire, manage, and develop global team members.
  • Ensure staff works efficiently and effectively to achieve project goals and objectives.
  • Coach and provide redirecting feedback as needed to employees.
  • Supervise 3 Data Scientists, 2 Data Engineers, and 1 Senior Software Engineer to design and develop machine learning infrastructure.

Monte Carlo Data helps businesses ensure the reliability of their data through end-to-end data observability, allowing users to monitor data freshness, volume, schema, and quality in real time. The platform includes tools for incident detection and resolution, which assist analysts in quickly addressing data quality issues, saving time and resources. 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 sectors.

Company Stage

Series D

Total Funding

$241M

Headquarters

San Francisco, California

Founded

2019

Growth & Insights
Headcount

6 month growth

11%

1 year growth

16%

2 year growth

44%
Simplify Jobs

Simplify's Take

What believers are saying

  • The $135M Series D funding round and a $1.6B valuation underscore strong investor confidence and provide ample resources for growth and innovation.
  • New features like Performance Monitoring and the Data Product Dashboard enhance the platform's value proposition, making it more attractive to data-dependent businesses.
  • Strategic partnerships with companies like Fivetran expand Monte Carlo's ecosystem, improving data reliability at scale and increasing market reach.

What critics are saying

  • The competitive landscape in data observability is intensifying, with rivals like Cribl and BigEye also vying for market share.
  • Rapid expansion and integration of new features may lead to operational challenges and potential service disruptions.

What makes Monte Carlo Data unique

  • Monte Carlo Data specializes in end-to-end data observability, offering real-time monitoring and incident resolution, which sets it apart from competitors like Cribl and BigEye.
  • The platform's seamless integration with popular communication tools like Slack, Teams, and JIRA enhances its usability and adoption across various business environments.
  • Monte Carlo's recent focus on vector databases and real-time streaming data, including integrations with Apache Kafka, positions it uniquely in the AI and data reliability market.
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