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

📌 Data Engineer (Azcapotzalco)
🏢 Programming.Com
📍 Azcapotzalco