Role: Data Engineer
Location: Remote (Requires occasional travel)
Responsibilities:
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Build and operate robust data pipelines for ingestion, cleaning, and transformation using Databricks, Airflow, or Dagster.
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Develop efficient ETL/ELT workflows in Python and SQL to support both batch and streaming workloads.
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Collaborate with ML and AI teams to deliver high-quality datasets for training, evaluation, and production features.
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Model and maintain structured data assets (Delta, Parquet, Iceberg) for reliability, versioning, and lineage tracking.
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Implement orchestration and monitoring — schedule jobs, track dependencies, and automate recovery from failures.
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Ensure data quality and compliance through validation frameworks, schema enforcement, and audit logging.
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Contribute to data platform evolution — evaluate tools, standardize best practices, and improve developer experience.
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Support performance and cost optimization across compute, storage, and orchestration systems.
Qualifications:
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3–6 years of experience as a Data Engineer or ETL Developer in a production environment.
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Proficiency in Python and SQL; strong familiarity with Databricks, Spark, or equivalent big-data frameworks.
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Experience with workflow orchestration tools such as Airflow, Dagster, Luigi or Prefect.
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Deep understanding of data modeling, data warehousing, and distributed data processing.
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Knowledge of modern data lakehouse architectures (Delta, Parquet, Iceberg).
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Familiarity with CI/CD, GitHub Actions, and data pipeline testing frameworks.
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Comfort working in a cross-functional environment with ML, product, and analytics teams.
Nice to Have:
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Experience with sports, telemetry, or sensor data pipelines.
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Familiarity with streaming frameworks (Kafka, Spark Structured Streaming, Flink).
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General knowledge of American football, the NFL, and college football
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Background in data governance, lineage, and observability tools (Monte Carlo, Great Expectations, Unity Catalog, OpenLineage).
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Experience with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
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Exposure to best practices in machine-learning model management and MLOps
