Senior Cloud Data & AI Solutions Architect on Azure

Tiger Analytics; Barcelona; Presencial; EUR 110.000 - 140.000; Jornada completa

Descripción de la vacante

Tiger Analytics is seeking a Sr. Solution Architect with extensive experience in data architecture, cloud services (Azure preferred), and AI-driven solutions. You will design scalable, secure data and AI platforms and collaborate with cross-functional teams.

Responsibilities include building end-to-end data architectures, defining ETL/ELT pipelines, implementing data governance and security, and mentoring engineers to adopt best practices.

Formación

• 14+ years in IT with 5+ years in solution or data architecture.
• Cloud solutions experience, Azure preferred.
• Data modelling, ETL/ELT, big data, governance and security.
• Experience with AI/ML and embedding into business processes.
• Excellent communication and problem-solving; Azure certs a plus.

Responsabilidades

• Design and implement scalable cloud-native solutions on Azure, ensuring security and performance.
• Architect end-to-end data solutions, integrating data sources, processing systems, and AI/ML platforms.
• Build conceptual & logical architectures for data-driven programs.
• Provide guidance on data pipelines, ETL/ELT workflows, and data integration.
• Implement data governance frameworks, ensuring data quality and compliance.
• Collaborate with stakeholders to understand requirements and deliver innovative solutions.
• Mentor team members and provide technical guidance where needed.

Conocimientos

Cloud architecture

Data architecture

AI/ML integration

Data governance

Data security

ETL/ELT design

Big data technologies

Leadership / mentoring

Herramientas

Azure

Azure Data Services

Descripción del empleo

Tiger Analytics is seeking a Sr. Solution Architect with extensive experience in data architecture, cloud services (Azure preferred), and AI-driven solutions. You will design scalable, secure data and AI platforms and collaborate with cross-functional teams.

Responsibilities include building end-to-end data architectures, defining ETL/ELT pipelines, implementing data governance and security, and mentoring engineers to adopt best practices.