Full-Time
Data Engineer
Confirmed live in the last 24 hours
Kumo
51-200 employees
Generates and deploys predictive models using AI
Mid
Mountain View, CA, USA
Hybrid position requires in-office presence.
- 4+ years of professional experience in SaaS/Enterprise companies
- Strong experience with data ingestion and connectors
- Experience in building end-to-end production-grade data solutions on AWS or GCP
- Experience in building scalable ETL pipelines
- Ability to plan effective data storage, security, sharing, and publishing within an organization
- Experience in developing batch ingestion and data transformation routines using ETL tools
- Familiarity with AWS services such as S3, Kinesis, EMR, Lambda, Athena, Glue, IAM, RDS
- Proficiency in several programming languages (Python, Scala, Java)
- Familiarity with orchestration tools such as Temporal, Airflow, Luigi, etc.
- Self-starter, motivated, with the ability to structure complex problems and develop solutions
- Excellent communication skills and ability to explain data and analytics strengths and weaknesses to both technical and senior business stakeholders
- Deep familiarity with Spark and/or Hive
- Understanding of different storage formats like Parquet, Avro, Arrow, and JSON and when to use each
- Understanding of schema designs like normalization vs. denormalization
- Proficiency in Kubernetes, and Terraform
- Azure, ADF and/or Databricks skills
- Experience with integrating, transforming, and consolidating data from various data systems into analytics solutions
- Good understanding of databases, SQL, ETL tools/techniques, data profiling and modeling
- Strong communications skills and client engagement
- Design, develop, and maintain data pipelines and ETL processes
- Collaborate with data scientists and analysts to understand data needs
- Ensure data quality and integrity throughout the data lifecycle
- Optimize data storage and retrieval processes
- Implement data security measures and best practices
- Monitor and troubleshoot data pipeline performance
- Document data processes and workflows
Kumo.ai specializes in creating and deploying predictive models that help organizations make accurate forecasts for critical tasks. Their platform uses Graph Neural Networks to analyze raw relational data, which means it can generate predictions without needing extensive manual data preparation. This approach leads to better accuracy and efficiency while also reducing infrastructure costs by eliminating the need for complex data processing systems. Kumo.ai's platform supports the entire Machine Learning lifecycle, from data preparation to model deployment, and is designed to deliver quick returns on investment for various applications, including customer retention and fraud detection. Unlike many competitors, Kumo.ai offers both Software as a Service and Private Cloud options, making it adaptable for businesses of all sizes. The goal of Kumo.ai is to provide reliable predictive insights that enhance decision-making and operational efficiency for its clients.
Company Stage
Series B
Total Funding
$54.5M
Headquarters
Mountain View, California
Founded
2021
6 month growth
↑ 24%1 year growth
↑ 36%2 year growth
↑ 97%
Simplify's Take
What believers are saying
- The partnership with Snowflake enhances Kumo.ai's scalability and ease of use, making it more attractive to data scientists.
- The recent $18 million Series B funding led by Sequoia Capital provides financial stability and resources for further innovation.
- Kumo.ai's SQL-like Predictive Querying Language simplifies model creation, enabling rapid deployment and broader adoption.
What critics are saying
- The competitive landscape in predictive AI is intense, with major players like Google and OpenAI posing significant threats.
- Reliance on partnerships, such as with Snowflake, could limit Kumo.ai's flexibility and independence.
What makes Kumo unique
- Kumo.ai leverages Graph Neural Networks to eliminate the need for manual feature engineering, setting it apart from traditional ML platforms.
- The platform's end-to-end capabilities, from data preparation to deployment, streamline the entire ML lifecycle, unlike competitors that require multiple tools.
- Kumo.ai's high availability SLAs and SOC2 compliance offer robust security and reliability, appealing to enterprise clients.
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