Data Engineer and Agentic AI Lead

Overview

We are seeking a Microsoft Fabric Data Engineer with Agentic AI experience to design, build, and maintain scalable data solutions within the Microsoft Fabric ecosystem. This role is responsible for developing robust data pipelines, implementing Medallion architecture (Bronze, Silver, Gold layers), and ensuring high-quality, analytics-ready datasets.

The ideal candidate will have hands-on experience with Microsoft Fabric components (Data Factory, OneLake, Lakehouse, Warehouse, and Power BI), strong data engineering fundamentals, and the ability to support advanced analytics and AI initiatives.

Key Responsibilities

• Design, develop, and maintain end-to-end data pipelines using Microsoft Fabric (Data Factory, Pipelines, Notebooks)
• Implement and manage Medallion architecture (Bronze, Silver, Gold layers) to ensure data quality, governance, and usability
• Ingest, transform, and integrate data from multiple structured and unstructured sources
• Optimize data workflows for performance, scalability, and cost efficiency
• Build and maintain Lakehouse and Data Warehouse solutions within Fabric
• Ensure data reliability through monitoring, logging, and error-handling mechanisms
• Collaborate with stakeholders to translate business requirements into scalable data solutions
• Enforce data governance, security, and compliance best practices
• Document data models, pipelines, and system architecture
• Contribute to roadmap planning and platform evolution

Database & Power BI Oversight

• Design enterprise-grade data models optimized for Power BI and analytics workloads
• Oversee creation and management of semantic models and datasets within Fabric
• Oversee dataset performance, refresh strategies, and capacity management in Power BI
• Implement data modeling best practices (star schema, normalization where appropriate)
• Support development of dashboards and reports by providing curated, high-quality datasets
• Manage access control, row-level security (RLS), and data lineage visibility
• Manage Power BI capacity, dataset refresh strategies, and scalability considerations
• Collaborate with BI developers to optimize DAX performance and reporting efficiency
• Monitor usage and adoption metrics to improve data accessibility and usability

AI Readiness & Advanced Analytics

• Prepare and structure data to support AI/ML use cases within Microsoft Fabric and Azure ecosystem
• Enable feature engineering by delivering clean, enriched, and well-governed datasets
• Support integration with AI tools such as Azure Machine Learning, Fabric Data Science, and Copilot experiences
• Implement data pipelines that support real-time or near real-time analytics where needed.
• Ensure datasets are scalable and aligned with AI model training requirements.
• Promote best practices for data versioning, reproducibility, and experimentation.

Qualifications

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related field.
• 7+ years of experience in data engineering or related roles.
• Hands-on experience with Microsoft Fabric or closely related tools (Azure Synapse, Azure Data Factory, Power BI).
• Strong expertise in Medallion architecture and modern data lake/lakehouse patterns.
• Proven experience building and maintaining data pipelines (ETL/ELT).
• Proficiency in SQL, Python, and/or Spark.
• Experience with Lakehouse architectures and distributed data processing.
• Strong understanding of data modeling and data warehousing concepts.

Desired Skills & Competencies

• Experience with OneLake, Delta Lake, and Fabric-native workloads.
• Experience with Data Agents, Copilot Studio and Microsoft Foundry
• Familiarity with CI/CD pipelines for data solutions.
• Power BI adoption and user satisfaction across business units.
• Delivery of AI-ready datasets and agentic AI pilot outcomes
• Security compliance and incident prevention

Key Competencies

• Strong problem-solving and analytical thinking
• Attention to detail and data quality focus
• Ability to work cross-functionally with technical and business teams
• Excellent communication and documentation skills
• Continuous learning mindset in evolving data and AI technologies

Performance Metrics

• Data integrity and availability across all departments
• System uptime and query performance
• Power BI adoption and user satisfaction across business units
• Reduction in time-to-action based on data insights
• Successful onboardin…