Position Summary
Our data engineering team is the foundation of this system, ensuring data is accurate, fast, and always available for our models and AI applications.
As a Data Engineer , you’ll design, build, and maintain the data pipelines that power our deep learning, video and LLM systems. You’ll work across ingestion, transformation, and orchestration layers — from real-time feeds to analytics-ready datasets. Your mission is to make data reliable, discoverable, and scalable for use by model training, analytics, and AI-driven products across multiple sports.
You’ll collaborate closely with our MLOps , and Sports Data teams to ensure seamless integration between data and AI.
Responsibilities
- Build and operate robust data pipelines for ingestion, cleaning, and transformation using Databricks, Airflow, or Kubernetes.
- Develop efficient ETL/ELT workflows in Python and SQL to support both batch and streaming workloads.
- Partner with ML/AI teams to make datasets and tools discoverable and safe for autonomous agents, including evaluation and guardrails for AI-generated queries.
- Develop retrieval pipelines (RAG, vector search) over structured stats and unstructured sources (scouting notes, video metadata) to power AI applications.
- Model and maintain structured data assets (Delta, Parquet, Iceberg) for reliability, versioning, and lineage tracking.
- Implement orchestration and monitoring: schedule jobs, track dependencies, and automate recovery from failures.
- Ensu