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 requiring 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
- Develop and maintain data ingestion and transformation processes
- Build and optimize scalable ETL pipelines
- Plan and implement data storage and security solutions
- Collaborate with technical and business stakeholders to communicate data insights
- Integrate and consolidate data from various systems into analytics solutions
Kumo.ai creates predictive models that help organizations with tasks like customer retention and fraud detection. Their platform uses Graph Neural Networks to analyze raw data, which allows for accurate predictions without manual data preparation. Kumo.ai's service covers the entire Machine Learning lifecycle and is available as Software as a Service (SaaS) or Private Cloud, making it suitable for various clients. The company aims to simplify predictive modeling and deliver a quick return on investment.
Company Stage
Series B
Total Funding
$35.5M
Headquarters
Mountain View, California
Founded
2021
6 month growth
↑ 29%1 year growth
↑ 34%2 year growth
↑ 105%
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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