Azure Data Architect

Requirements

• Proven experience as a Data Architect with a strong hands-on technical background.
• Solid experience with Microsoft Azure and the design and implementation of cloud-based data architectures.
• Strong hands-on experience with Azure Databricks.
• Advanced knowledge of Python for data processing and engineering.
• Strong experience with PySpark and distributed data processing.
• Solid understanding of Data Architecture, including data models, data pipelines, data integration and processing patterns.
• Experience designing and implementing scalable and robust data pipelines.
• Ability to translate business and technical requirements into effective data architecture solutions.
• Strong understanding of Data Engineering best practices, including performance, scalability, data quality, security and governance.
• Ability to work in a highly hands-on role, contributing directly to the design and implementation of technical solutions.
• Good level of English, both written and spoken, for communication in an international environment.
• Preferably based in Northern Portugal, with availability for occasional visits to the office when required.
• Experience working in Agile/Scrum environments is a plus.

Responsibilities

• Design and implement scalable and robust data architectures on Microsoft Azure.
• Design, develop and optimize data solutions using Azure Databricks, Python and PySpark.
• Develop and maintain efficient data pipelines for data ingestion, transformation and processing.
• Ensure the scalability, performance, reliability and maintainability of data solutions.
• Define and promote data architecture standards, patterns and best practices.
• Collaborate closely with Data Engineers, Software Engineers, Architects and other technical stakeholders.
• Analyze functional and technical requirements and translate them into appropriate data architecture solutions.
• Identify opportunities to improve and optimize existing data architectures and pipelines.
• Ensure the implementation of best practices around data quality, security, governance and performance.
• Troubleshoot and resolve complex technical challenges across the data platform.
• Contribute to technical decision-making and architectural discussions.
• Remain actively involved in the implementation, maintaining a strong hands-on approach rather than focusing solely on architectural design.