7 publicaciones de empleo destacadas de data engineer en industrial vallejo, cdmx (¡Están contratando!)

Discover related jobs

Based on your search for data engineer jobs.

Analista de Customer Success
APPVANTGARDE —Tlalcalli, Méx.
Mantener actualizadas las bases de datos y bases de conocimiento con información técnica, incidencias y soluciones relevantes.
$16,500 a $17,000 por mes
Postulación rápida
Analista JR de Seguridad Social y Nomina Finiquitos
Tiendas 3B —Ciudad de México, CDMX
Buscamos a alguien con enfoque analítico, atención al detalle y gusto por trabajar con datos y procesos administrativos. Oportunidad de obtener beca de estudio.
$13,000 a $18,000 por mes
Postulación rápida
10d

Al ingresar a tu cuenta, aceptas las Condiciones del servicio de SimplyHired y das tu consentimiento a nuestra Política de privacidad y Cookies.

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