Overview
Data Foundation covers the full spectrum of data engineering and architecture talent — from building ETL pipelines and lakehouse platforms to designing scalable data architectures that serve the entire AI stack. These roles form the backbone of any modern AI operation, ensuring that models have clean, accessible, and performant data to train and serve.
Roles We Place
- Data Engineers
- Data Architects
- Analytics Engineers
- Data Platform Engineers
- BI Engineers
Tech Stack
Spark, Kafka, Airflow, Snowflake, Databricks, dbt, BigQuery, Redshift, Delta Lake, Fivetran
Typical Hiring Scenarios
Building a lakehouse from scratch for a Series B AI startup — You've closed funding and need to transition from ad-hoc analytics to a structured, scalable data foundation. We help you hire a data architect who understands modern cloud-native patterns (medallion architecture, Delta Lake, etc.) and can set you up for rapid scaling.
Migrating legacy ETL to modern streaming pipelines — Your data infrastructure is slowing down. You need experienced Data Engineers who can design and execute a migration to Kafka + Spark, or build real-time pipelines on Databricks, without disrupting production.
Hiring a Data Architect to design a unified data platform across 3 cloud providers — You operate in AWS, Azure, and GCP. You need a Data Architect who can unify your data layer across cloud providers, enforce data governance, and design for cost-efficiency at scale.