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