Come and change the world of AI with the Kumo team!
Companies spend millions of dollars to store terabytes of data in data lakehouses, but only leverage a fraction of it for predictive tasks. This is because traditional machine learning is slow and time consuming, taking months to perform feature engineering, build training pipelines, and achieve acceptable performance.
At Kumo, we are building a machine learning platform for data lakehouses, enabling data scientists to train powerful Graph Neural Net models directly on their relational data, with only a few lines of declarative syntax known as Predictive Query Language. The Kumo platform enables users to build models a dozen times faster, and achieve better model accuracy than traditional approaches.
We're seeking intellectually curious and highly motivated Data Engineers to become foundational members of our Machine Learning and Data Platform team.
Your Foundation:
- 1+ 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.
Your Extra Special Sauce:
- 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
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Top Skills
What We Do
Democratizing AI on the Modern Data Stack!
The team behind PyG (PyG.org) is working on a turn-key solution for AI over large scale data warehouses. We believe the future of ML is a seamless integration between modern cloud data warehouses and AI algorithms. Our ML infrastructure massively simplifies the training and deployment of ML models on complex data.
With over 40,000 monthly downloads and nearly 13,000 Github stars, PyG is the ultimate platform for training and development of Graph Neural Network (GNN) architectures. GNNs -- one of the hottest areas of machine learning now -- are a class of deep learning models that generalize Transformer and CNN architectures and enable us to apply the power of deep learning to complex data. GNNs are unique in a sense that they can be applied to data of different shapes and modalities.
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