Ingeniero de Datos

Role: Data Engineer

Location: Remote (Requires occasional travel)

Responsibilities:

• Build and operate robust data pipelines for ingestion, cleaning, and transformation using Databricks, Airflow, or Dagster.
• Develop efficient ETL/ELT workflows in Python and SQL to support both batch and streaming workloads.
• Collaborate with ML and AI teams to deliver high-quality datasets for training, evaluation, and production features.
• 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.
• Ensure data quality and compliance through validation frameworks, schema enforcement, and audit logging.
• Contribute to data platform evolution — evaluate tools, standardize best practices, and improve developer experience.
• Support performance and cost optimization across compute, storage, and orchestration systems.

Qualifications:

• 3–6 years of experience as a Data Engineer or ETL Developer in a production environment.
• Proficiency in Python and SQL; strong familiarity with Databricks, Spark, or equivalent big-data frameworks.
• Experience with workflow orchestration tools such as Airflow, Dagster, Luigi or Prefect.
• Deep understanding of data modeling, data warehousing, and distributed data processing.
• Knowledge of modern data lakehouse architectures (Delta, Parquet, Iceberg).
• Familiarity with CI/CD, GitHub Actions, and data pipeline testing frameworks.
• Comfort working in a cross-functional environment with ML, product, and analytics teams.

Nice to Have:

• Experience with sports, telemetry, or sensor data pipelines.
• Familiarity with streaming frameworks (Kafka, Spark Structured Streaming, Flink).
• General knowledge of American football, the NFL, and college football
• Background in data governance, lineage, and observability tools (Monte Carlo, Great Expectations, Unity Catalog, OpenLineage).
• Experience with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
• Exposure to best practices in machine-learning model management and MLOps