Principal Data Engineer - AI

Overview

Principal Data Engineer - AI responsible for setting technical direction for ingesting, transforming, storing, serving, and governing data across the full stack of a data platform.

What you'll do

• Lead data architecture, design, and deployment of scalable, high-throughput big data systems.
• Architect and manage foundational data systems for AI infrastructure, including vector, NoSQL, and document databases.
• Build end-to-end data engineering solutions including ETL/ELT pipelines, API services, and ingestion frameworks.
• Design storage and processing layers for analytics workloads (data lakes, warehouses, distributed file systems, real-time streaming).
• Optimize and scale distributed queries and data transformations for high performance and low latency.
• Implement data quality frameworks to ensure data integrity, reliability, and governance.

What you'll need

• Extensive data engineering experience with hands-on delivery in complex data environments.
• Deep practical understanding of database ecosystems powering AI/ML infrastructure (vector, NoSQL, and document stores).
• Hands-on experience building and shipping large-scale data platforms in production.
• Deep practical experience with distributed data processing frameworks (Apache Spark, Flink, Hadoop).
• Experience with message brokers and event streaming platforms (Apache Kafka, Kinesis).
• End-to-end exposure to data pipeline lifecycle development with workflow orchestration tools (Apache Airflow, Dagster).
• Advanced SQL skills and proficiency in Python.

Details

• Open to candidates in Eastern or Central time zones.
• Work is hybrid with onsite work expected two days per week for employees within commuting distance of an office.