{"id":3817993,"url":"https://alion.io/job/yel-hub-leader-data-engineering-ai-software-engineering-office-caba-ref-1074","title":"Hub Leader Data Engineering – AI Software Engineering Office - caba ref 1074","company":{"id":4516451,"name":"Yel","domain":null,"url":"https://alion.io/company/yel-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Buenos Aires, Argentina"],"countries":["AR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure 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Services con alcance global, buscamos un Hub Leader Data Engineering AI Software Engineering Office para sumarse al equipo de profesionales.\n· Ubicación: Ciudad de Buenos Aires \n Modalidad: Híbrida | LunVie de 8 a 17 hs \n· ¿Qué buscamos en el rol a desempeñar?\n· Objetivo: Acelerar el desarrollo en plataformas de datos y analítica, estandarizando pipelines, calidad y operación de modelos (DataOps + MLOps) en arquitectura lakehouse.\nReporte a head of SEO, rol matricial.\n· Responsabilidades\n Diseñar, desarrollar y mantener aceleradores de productividad para los equipos de ingeniería de datos, incluyendo templates, frameworks, componentes reutilizables, librerías, Golden Paths y automatizaciones que reduzcan el esfuerzo de desarrollo.\n Evaluar, seleccionar e incorporar herramientas de IA que incrementen la productividad del ciclo de desarrollo de soluciones de datos, habilitando capacidades como generación asistida de SQL, dbt, documentación, pruebas y análisis exploratorio mediante agentes de IA.\n Definir estándares corporativos de DataOps, MLOps y Data Quality, estableciendo lineamientos, activos reutilizables y capacidades comunes para CI/CD, observabilidad, calidad, versionado, monitoreo y gobierno de datos, facilitando su adopción por parte de los equipos de desarrollo.\n Desarrollar e integrar capacidades compartidas de gobierno, catálogo, trazabilidad y seguridad de datos que puedan ser reutilizadas por los diferentes equipos de ingeniería, asegurando consistencia tecnológica y cumplimiento de las políticas corporativas.\n Realizar pruebas de concepto y evaluar nuevas tecnologías relacionadas con ingeniería de datos e Inteligencia Artificial, definiendo recomendaciones y planes de adopción para la organización.\n Trabajar en conjunto con proveedores tecnológicos y equipos internos para implementar y evolucionar las capacidades de la plataforma de ingeniería de datos, asegurando su correcta integración con el ecosistema corporativo.\n Definir e implementar métricas que permitan medir la adopción, productividad e impacto de las capacidades desarrolladas, identificando oportunidades de mejora continua y promoviendo la evolución de las prácticas de ingeniería.\n Brindar acompañamiento técnico y mentoring a los equipos de desarrollo durante la adopción de nuevas herramientas, frameworks y prácticas de ingeniería de datos, actuando como referente técnico de la especialidad.\n· Requisitos imprescindibles \n +10 años en data engineering productivo.\n Experiencia comprobable con pipelines en producción (Spark, dbt o equivalente) procesando volúmenes reales (TB+).\n Casos documentados de mejora en calidad de datos (reducción de incidentes, SLAs cumplidos).\n Dominio práctico de al menos uno de los Big Three: Databricks, Snowflake o Microsoft Fabric.\n Experiencia con automatización de pipelines y CI/CD para datos (Azure DevOps, GitHub Actions, dbt Cloud, Liquibase para esquemas).\n SQL avanzado y un lenguaje de programación de datos 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