Data Architect (Microsoft Fabrics)

Preferred Qualifications

• Strong Microsoft Fabric / Azure Data Architect profile
• Mandatory (Experience 1) – Must have 8+ years of experience in Data Architecture / Data Engineering, with strong exposure to enterprise-scale data platform modernization initiatives
• Mandatory (Experience 2) – Must have 3+ years of deep hands-on experience in Microsoft Fabric ecosystem including Fabric Lakehouse, OneLake, and Data Factory, with large-scale implementations
• Mandatory (Experience 3) – Strong expertise in designing and implementing Medallion (Bronze/Silver/Gold) architecture and scalable lakehouse platforms supporting batch and real-time workloads
• Mandatory (Experience 4) – Strong experience in Azure data ecosystem including Azure Data Factory, Azure Synapse, ADLS, and Power BI, with good understanding of cloud-native data architectures
• Mandatory (Experience 5) – Proven experience designing scalable data models including Dimensional Modelling (Star/Snowflake) and/or Data Vault for enterprise data warehouses
• Mandatory (Experience 6) – Must have hands-on experience building ingestion pipelines including batch, streaming, and CDC pipelines using tools like Spark, Kafka, or Fabric pipelines
• Mandatory (Experience 7) – Strong experience in implementing data governance frameworks including data cataloging, lineage, metadata management, and security controls
• Mandatory (Skill 1) – Proven experience in building CI/CD pipelines for data platforms using Azure DevOps / Git, including automated deployment and environment management
• Mandatory (Skill 2) – Hands-on experience designing AI/ML-ready data platforms, enabling advanced analytics, machine learning, and Generative AI use cases
• Mandatory (Skill 3) – Experience with orchestration and workflow tools, and integrating data platforms with BI tools like Power BI for enterprise reporting
• Mandatory (Note) – Only immediate joiners (within 15 days) will be considered
• Preferred (Certification) – Microsoft Fabric / Azure certifications; exposure to MDM, data quality frameworks, and enterprise metadata tools