Senior Data Engineer supporting AI - enabled financial compliance initiative with data pipelines and ingestion processes. Collaborating with diverse teams in a mission - critical regulated environment.
About the role
- Data Engineer designing data platforms in Azure for banking use cases at Stefanini. Responsible for data ingestion, modeling, and reliability, enabling advanced analytics and reporting practices.
Responsibilities
- Diseña, construye y opera plataformas y pipelines de datos escalables, seguros y auditables en Azure para casos de uso bancarios. Es responsable de la ingesta, estandarización, modelado, performance y confiabilidad del dato, habilitando analítica avanzada/ML y reporting bajo prácticas DataOps.
Requirements
- Diseña, construye y opera plataformas y pipelines de datos escalables, seguros y auditables en Azure para casos de uso bancarios. Es responsable de la ingesta, estandarización, modelado, performance y confiabilidad del dato, habilitando analítica avanzada/ML y reporting bajo prácticas DataOps.
- Diseñar arquitectura de datos en Azure (ingesta → raw → curated → serving) con enfoque lakehouse y/o DWH según el caso.
- Construir pipelines batch y near-real-time con ADF/Synapse Pipelines (y/o Databricks) incluyendo cargas incrementales, CDC, y manejo de errores.
- Modelar datos (dimensional y/o data vault, según dominio) para consumo por el Data Scientist, BI y riesgos/compliance.
- Implementar transformaciones y validaciones usando SQL, Python (pandas) y KNIME cuando se requiera (workflows reproducibles, parametrizados).
- Optimizar performance/costos: particionamiento, file formats (Parquet/Delta), compaction, caching, tuning de queries y cargas.
- Asegurar observabilidad: métricas, logs, alertas, SLAs/SLOs y “runbooks” operacionales.
- Implementar estándares de seguridad: RBAC, Key Vault, managed identities, private endpoints, segregación por entornos.
- Colaborar con Gobernanza para linaje, catálogo, retención y clasificación; y con el Data Scientist para feature datasets y scoring pipelines.
- Definir y mantener CI/CD para artefactos de datos (infra como código, pipelines, notebooks, tests).
Benefits
- Habilidades Técnicas Requeridas:
- Azure Data
- ADLS Gen2, Azure SQL / MI, Synapse Analytics
- Azure Data Factory / Synapse Pipelines.
- Azure Databricks (preferible) y fundamentos de Spark (plus).
- Mensajería/streaming: Event Hubs / Kafka (plus según necesidad).
- Ingeniería de Datos
- SQL avanzado (modelado, performance, ventanas, optimización).
- Python (pandas, pyarrow, testing), notebooks.
- Data formats: Parquet/Delta; particionamiento y esquemas evolutivos.
- Orquestación y automatización; control de versiones (Git) y CI/CD (Azure DevOps).
- Calidad & Operación
- Pruebas de datos (Great Expectations o equivalente), reconciliaciones, DQ rules.
- Observabilidad: logging/monitoring (Application Insights/Log Analytics), alertas Entregables Típicos.
- Pipelines productivos con SLAs (ingesta, transformación, serving).
- Data models (curated/semantic), data marts y datasets de features.
- Documentación técnica (arquitectura, diccionario, runbooks, diagramas).
- Framework de pruebas, monitoreo y control de costos confiabilidad del pipeline, modelos de datos, performance, operación y CI/CD.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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