Monte Carlo Data

Data Engineer

United States

$180,000 – $220,000Compensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Data Management, AI, SaaSIndustries

Requirements

Candidates should have over 5 years of experience building production-grade data pipelines and backend services, with strong expertise in Python and SQL. Familiarity with AWS technologies and modern data warehouses such as Snowflake, BigQuery, or Redshift is required. Experience with distributed architectures, an ownership mindset, urgency, and customer focus are also essential. PySpark experience is a plus, as is experience with Spark, Airflow, and Infrastructure as Code (IaC) solutions on AWS, Databricks, and Snowflake.

Responsibilities

The Data Engineer will be responsible for ingesting and transforming raw customer data into high-quality datasets. They will design, build, and maintain scalable data pipelines and orchestration workflows, and preprocess metrics and metadata for anomaly detection and lineage models. This role involves collaborating with Data Science and Analytics teams to productionize models and dashboards, continuously improving pipeline reliability by using Monte Carlo's product, and managing and creating data platform resources like database objects and AWS resources.

Skills

Data Pipelines
Backend Services
Python
SQL
PySpark
AWS
Snowflake
BigQuery
Redshift
Distributed Architectures
Spark

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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