• Build and maintain high-performance pipelines for trillions of tokens. • Deliver diverse and high quality datasets for pre-training foundation models. • Closely work with other teams such as Pretraining, Posttraining, Evals and Product to to ensure alignment on the quality of the models delivered.

• Design, build, and maintain end-to-end data pipelines using Databricks (SparkSQL, PySpark) for data ingestion, transformation, and processing. • Integrate data from various structured and unstructured sources, including medical imaging systems, EMRs, Change-Data-Capture from SQL Databases, and external APIs. • Collaborate with the analytics team to create, optimize, and maintain dashboards in Looker. • Deploy and manage cloud-based solutions on AWS (e.g., S3, EMR, Lambda, EC2) to ensure scalability, availability, and cost-efficiency using IaC tooling (Terraform and Databricks Asset Bundles). • Oversee MongoDB and PostgreSQL databases, including schema design, indexing, and performance tuning. • Adhere to healthcare compliance requirements (e.g., HIPAA) and best practices for data privacy and security. • Work cross-functionally with data scientists, product managers, and other engineering teams to gather requirements and define data workflows.

• Build and maintain robust data pipelines using tools like Airflow, Airbyte, and BigQuery. • Manage and evolve our data warehouse architecture. • Integrate third-party platforms including Salesforce, HubSpot, Iterable, ZocDoc, and Metabase. • Lead the implementation of centralized data governance, defining sources of truth and managing data flow standards. • Partner with cross-functional teams to implement clean, reporting-ready data models in dbt. • Write clean, production-grade code in a general-purpose language (e.g., Python). • Monitor data quality and ensure reliability across systems.

• Lead, mentor, and grow a team of data engineers • Partner with cross-functional teams to deliver trustworthy, actionable data • Drive the long-term vision for data engineering practices • Enable AI-driven approaches to improve development efficiency