Data & ML Engineering Manager
hace 4 días
Como
Data & ML Engineering Manager
, liderarás y escalarás los equipos de Ingeniería de Datos e Ingeniería de Machine Learning para entregar soluciones seguras, confiables y alineadas al negocio. Serás el referente técnico y estratégico que diseñe plataformas de datos escalables y pipelines de ML que soporten analítica avanzada, motores de decisión y modelos de riesgo crediticio.Tu rol combina excelencia técnica, gobierno de datos y liderazgo de personas, asegurando que la estrategia de datos se traduzca en impacto real para la organización.
Actividades principales:
- Liderazgo y gestión de equipos
: definir visión y prioridades de Data & ML Engineering, desarrollando talento técnico de alto nivel. - Arquitectura y datos
: supervisar pipelines batch y streaming en Databricks, Spark y Delta Lakehouse; asegurar ingesta, transformación y calidad a lo largo de las capas Bronze → SilverDQ → SilverDM → Gold. - Machine Learning en producción
: liderar el ciclo de vida de modelos (entrenamiento, validación, despliegue, monitoreo, reentrenamiento) con MLflow y feature stores. - Gobernanza y cumplimiento
: establecer contratos de datos, validaciones, detección de anomalías y lineamientos de seguridad (ABAC/RBAC, PCI DSS, GDPR equivalentes). - Alineación estratégica
: trabajar con Riesgo, Finanzas, IT y Producto para traducir necesidades de negocio en soluciones de datos y ML. - Innovación y mejora continua
: impulsar modernización de plataformas (Photon, Mosaic AI, LLMs), nuevas prácticas de DataOps/MLOps y automatización de procesos.
Si cumples con lo siguiente, aplica:
- Experiencia consolidada liderando equipos de
Data Engineering y ML Engineering
. - Conocimiento avanzado en
Databricks, Spark, Delta Lake, MLflow, Unity Catalog
. - Dominio de
Python, PySpark, SQL
, pipelines distribuidos y CI/CD. - Experiencia en MLOps, feature stores y despliegue de modelos en producción.
- Familiaridad con gobierno de datos, contratos de datos y frameworks de calidad (DAMA-DMBOK).
- Habilidad para conectar estrategia de datos con resultados de negocio, especialmente en
riesgo crediticio y servicios financieros
. - Liderazgo probado para coordinar stakeholders técnicos y de negocio.
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