Data Engineer

• Develop and execute the organization’s data strategy and architecture aligned with business objectives
• Translate business requirements into scalable data architecture, models and technical specifications
• Design and manage data models, metadata, lineage and data catalogs
• Architect and integrate batch and real-time data pipelines across diverse sources and platforms
• Establish and enforce data governance, quality, security, privacy and regulatory standards
• Design and deploy scalable cloud and hybrid data platforms, optimizing performance and cost
• Implement data profiling, validation, cleansing and lifecycle management processes
• Monitor, troubleshoot and optimize data platforms, pipelines and infrastructure
• Collaborate with business leaders, engineers and data scientists to deliver data solutions
• Lead data architecture initiatives and mentor teams on standards, tools and best practices
• Evaluate emerging data, cloud, big data and AI technologies and recommend relevant solutions
• Provide expert services remotely while integrated into the company team collaborating closely with a major client’s on-site IT team

Data Architect

• Analyze, process, and interpret large datasets to generate actionable insights
• Develop and maintain ingestion and transformation processes
• Design data warehouse models

Data Warehouse Engineer

• Develop and maintain data ingestion pipelines using Microsoft Fabric
• Design and implement data models across the Bronze, Silver and Gold layers
• Transform and prepare data for analytics and reporting
• Collaborate with the team to support the development of data visualization and reporting solutions
• Contribute to the implementation and evolution of the data platform throughout the MVP phase
• Integrate data from three source systems
• Build Bronze, Silver and Gold data layers
• Prepare data for analytics and reporting

Data Engineer, Microsoft Fabric

• Design and implement modern data platforms and data engineering solutions, including data pipelines, lakehouse architectures, semantic data models, and data products
• Bridge Data Engineering and AI Engineering, focusing on RAG architectures, Conversational BI, AI-powered data products, and LLM-based applications
• Develop and integrate AI agents and agentic components for pipeline development, data quality, automation, monitoring, and self-service analytics
• Use Microsoft Azure, Microsoft Fabric, Databricks, Snowflake, and dbt to build scalable data and AI solutions
• Design end-to-end architectures across the data lifecycle, from source systems and integration through transformation, governance, quality assurance, and consumption
• Advise clients on technology selection, target architectures, migration strategies, platform modernization, and AI integration into data strategies
• Own workstreams or projects, ensuring pragmatic execution, technical excellence, and measurable business value
• Contribute best practices, reusable assets, technical standards, and innovation initiatives to the Data & AI portfolio

Senior Consultant, AI Data Engineering – Microsoft Azure

• Analyze, process, and interpret large datasets to generate actionable insights
• Develop and maintain ingestion and transformation processes
• Design data warehouse models
• Work on international projects with leading global clients
• Collaborate within cross-functional engineering teams

• Analyze, process, and interpret large datasets to generate actionable insights
• Develop and maintain ingestion and transformation processes
• Design data warehouse models
• Work on international projects with leading global clients
• Travel as related to international projects

• Analyze, process, and interpret large datasets to generate actionable insights
• Develop and maintain ingestion and transformation processes
• Design data warehouse models
• Work on transformative projects and emerging technologies for global companies
• Collaborate within cross-functional engineering teams