Senior Data Engineer - Azure & AI Platform (Remote, Contractor)

About the role

A remote, project-based engagement seeks an experienced Senior Data Engineer to lead the development of a scalable, cloud-based data engineering platform with AI/ML capabilities. The contractor takes full ownership of the engineering lifecycle, from architecture and development to testing, optimization and deployment, with considerable technical autonomy.

Key responsibilities

• Design and build scalable Azure cloud-native data architectures, including batch and real-time processing, and integrate cloud services, databases and external applications.
• Architect and implement end-to-end ETL/ELT pipelines, data ingestion, transformation, validation and orchestration, and optimize performance.
• Build data preparation and transformation workflows supporting machine learning and AI, and ensure data quality throughout the AI/ML lifecycle.
• Develop backend services, RESTful APIs, event-driven processing and application components.
• Set up CI/CD pipelines, source control, release management, automated testing and performance validation, and maintain technical documentation.

Required profile

• 7+ years of professional software or data engineering experience.
• Strong hands-on experience with Microsoft Azure and cloud architectures, and proven ability to design complex data solutions.
• Experience with production-grade ETL/ELT pipelines, distributed processing (Spark, Databricks) and AI/ML data workflows.
• Ability to make architectural decisions, manage priorities and deliver a complete solution independently.

Required skills

• Azure, Azure Data Factory, Azure Databricks, Azure SQL, Azure Functions, Cosmos DB, Event Hubs, Event Grid, Service Bus
• Python, SQL/T-SQL, C#/.NET, Java, Node.js, Unix shell scripting
• ETL/ELT, Apache Spark, Spark SQL, Hive SQL, data modeling, relational databases
• Azure DevOps, Git/GitHub, RESTful APIs, JSON/XML, CI/CD automation

To apply, candidates share their Azure and data engineering experience, examples of platforms or pipelines they built, AI/ML data infrastructure experience, primary languages and cloud tools, availability and timeline, and a GitHub profile or portfolio if available.