Ingeniero de Datos
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Job Description
Data Engineer responsible for building data solutions for banking clients. Collaborating with product, BI, and engineering teams for data quality and governance.
Responsibilities:
- Serás responsable de diseñar, construir y operar soluciones de datos para un cliente del sector bancario, habilitando analítica, reporting regulatorio/gerencial, modelos de riesgo y casos de negocio.
- Trabajarás con equipos de producto, BI, analítica, ingeniería y seguridad para garantizar calidad, disponibilidad, trazabilidad y gobierno de los datos.
- Diseñar y desarrollar pipelines ETL/ELT (batch y/o streaming) para ingestión, transformación y publicación de datos.
- Implementar y mantener modelos de datos en data lake / data warehouse (modelos dimensionales, data marts, capas bronze/silver/gold).
- Optimizar performance de consultas y procesos (particionado, clustering, manejo eficiente de archivos/tablas).
- Asegurar calidad de datos (validaciones, reconciliaciones, reglas de negocio, monitoreo de pipelines).
- Implementar linaje, catálogo y documentación de datasets, definiendo estándares de nombres y metadatos.
- Gestionar accesos y seguridad de datos (principio de mínimo privilegio, cifrado, clasificación de datos sensibles).
- Monitorear pipelines y plataformas, gestionar incidentes y establecer SLAs/controles operativos.
- Automatizar despliegues con CI/CD e Infraestructura como Código cuando aplique.
- Levantar requerimientos con áreas de negocio (canales, riesgo, finanzas, operaciones) y traducirlos a soluciones de datos.
- Colaborar con analistas/DS/BI para habilitar datasets confiables y reutilizables.
Requirements:
- +3 a 5 años de experiencia en ingeniería de datos en entornos productivos.
- Experiencia comprobada en sector bancario/financiero (datos transaccionales, auditoría, calidad, trazabilidad).
- Dominio de SQL avanzado (optimización, ventanas, CTEs, modelado).
- Experiencia con al menos una tecnología de procesamiento: Spark / PySpark (deseable fuerte) o Python para procesamiento (pandas) en pipelines orquestados.
- Experiencia con herramientas de orquestación: Airflow, Prefect, Dagster u otras.
- Manejo de data warehouse/lakehouse: Snowflake, BigQuery, Redshift, Databricks, Synapse (según stack).
- Conocimiento de formatos y almacenamiento: Parquet/ORC, particionado, compresión (Deseable) Delta Lake / Iceberg / Hudi.
- Experiencia con control de versiones Git y prácticas de entrega continua (CI/CD deseable).
- Buenas prácticas de seguridad y gobierno de datos.
- Deseables (no excluyentes) Streaming y mensajería: Kafka/Kinesis/PubSub.
- Nube (ideal AWS): S3, Glue, Athena, EMR, Redshift, Lake Formation (o equivalentes).
- Data quality frameworks: Great Expectations, Deequ u otros.
- Conocimiento de regulaciones/procesos bancarios: conciliaciones, riesgo, AML/KYC, reportes, auditoría.
- Experiencia con herramientas de catálogo/linaje: DataHub, Collibra, Amundsen, OpenMetadata.
- Modelado orientado a analítica: Kimball, Data Vault (deseable).
Benefits:
- Salario competitivo acorde a tu experiencia.
- Desarrollo profesional : apoyo para formación continua y certificaciones.
- Bonificaciones por desempeño.
- Un ambiente centrado en tu crecimiento humano y profesional.
- Proyectos retadores con impacto real en banca y transformación digital.
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