We are looking for an experienced Microsoft Fabric Lead Engineer to lead the design, development, implementation, and support of enterprise-scale data and analytics platforms. The ideal candidate will have strong expertise in Microsoft Fabric, Azure Data Platform, data engineering, cloud data architecture, and technical leadership, with experience delivering scalable, secure, and high-performing enterprise data solutions.

The role will be responsible for architecture decisions, engineering standards, technical delivery, team mentorship, and modernization of enterprise data platforms using Microsoft Fabric.

Key Responsibilities:

- Lead the end-to-end design, development, implementation, and support of Microsoft Fabric data and analytics solutions.

- Define technical architecture, engineering standards, reusable patterns, and best practices for enterprise Fabric adoption.

- Provide technical leadership to Data Engineers and Developers, including design reviews, code reviews, technical guidance, and mentoring.

- Design and implement enterprise data platforms using OneLake, Lakehouse, Data Warehouse, Data Factory, Data Engineering, Real-Time Intelligence, and Power BI.

- Architect scalable cloud-native data solutions supporting analytics, business intelligence, and AI/ML workloads.

- Design and develop robust ETL/ELT pipelines using Fabric Data Factory, Spark, and notebooks.

- Integrate structured, semi-structured, and unstructured data from cloud and on-premises sources.

- Implement data transformation, cleansing, validation, orchestration, and ingestion strategies with a focus on reliability and performance.

- Design dimensional models, Lakehouse architectures, and enterprise data warehouse solutions.

- Develop semantic models and datasets to support Power BI reporting and self-service analytics.

- Optimize Spark workloads, SQL Warehouse performance, data processing, partitioning, caching, and storage utilization.

- Monitor platform performance and proactively identify and resolve scalability, performance, and reliability issues.

- Implement enterprise security using Microsoft Entra ID, RBAC, and Fabric workspace security.

- Establish data governance practices covering data quality, lineage, cataloging, auditing, access control, and compliance.

- Implement CI/CD and DevOps practices for Fabric using Azure DevOps and/or GitHub.

- Automate deployment of Fabric artifacts across Development, QA, UAT, and Production environments.

- Lead production deployments, platform monitoring, incident management, troubleshooting, and post-production support.

- Define and support disaster recovery, business continuity, and platform resilience strategies.

- Enable AI and advanced analytics capabilities within Microsoft Fabric and collaborate with Data Science and AI teams to operationalize analytical solutions.

- Partner with Enterprise Architects, Product Owners, Business Stakeholders, and cross-functional technology teams to translate business requirements into scalable technical solutions.

- Participate in architecture review boards, technical governance forums, and solution design discussions.

- Communicate architecture decisions, technical risks, delivery status, and platform strategy to senior stakeholders.

- Drive continuous improvement, engineering excellence, and adoption of modern data engineering practices.

Required Skills & Experience:

- 8 - 13 years of experience in Data Engineering, Data Warehousing, Cloud Data Platforms, or related technologies.

- 4+ years of hands-on experience with Microsoft Azure Data Platform technologies.

- 2+ years of hands-on experience with Microsoft Fabric, preferably in enterprise implementations.

- Strong expertise in Microsoft Fabric architecture and engineering.

- Hands-on experience with OneLake, Lakehouse, Data Warehouse, Data Factory, Data Engineering, Real-Time Intelligence, and Power BI.

- Strong proficiency in SQL, Python, PySpark, and Apache Spark.

- Strong understanding of Spark performance optimization and distributed data processing.

- Experience designing ETL/ELT pipelines, data models, data warehouses, and Lakehouse architectures.

- Experience with ADLS, Azure DevOps, GitHub, source control, and CI/CD pipelines.

- Strong understanding of data governance, security, access control, data quality, lineage, and compliance.

- Experience leading technical teams and driving architecture and engineering decisions.

- Proven experience delivering large-scale enterprise data modernization or transformation programs.

- Strong troubleshooting, analytical, communication, and stakeholder-management skills.

- Ability to work effectively across architecture, engineering, business, and leadership teams.