Data Engineer

Role Summary

Build robust, observable data pipelines that power research and production AI. Success means high pipeline reliability (on-time SLAs), strong data quality (validation & lineage), and enabling fast experimentation. You will partner with AI/ML, analytics, and product to make data trustworthy and usable.

Responsibilities

• Architect and operate batch/stream pipelines (Airflow; Spark optional) for ETL/ELT.
• Model/manage schemas; enforce data quality and lineage/governance.
• Support ML workflows with DVC (data versioning) and MLflow or Weights & Biases.
• Build feature stores/data services; expose datasets via secure REST endpoints.
• Optimize performance/cost across storage/compute; implement monitoring/alerting.
• Maintain documentation and internal catalogs; enable self-service analytics.

Qualifications

• Skills: Programming in C++ or Java; SQL & NoSQL; Pandas/NumPy; PySpark; Airflow; API development; Docker.
• MLOps: DVC; MLflow or W&B; model packaging/deployment fundamentals.
• Cloud: AWS SageMaker, Azure ML, or GCP AI experience.
• Nice to have: Unreal Engine exposure.
• Environment: Solid Linux background for development and deployment.
• Education/Experience: Proven experience building reliable pipelines in production