Data & AI Architect (Azure, Microsoft Fabric, Databricks)

We are seeking a

• Design and define end-to-end enterprise data architectures using Microsoft Fabric, Azure Databricks, Azure Data Factory, Azure Data Lake, Synapse, and related cloud services.
• Translate business requirements into scalable, secure, and maintainable data architecture and solution designs.
• Define architecture patterns for data ingestion, transformation, storage, processing, serving, analytics, and reporting.
• Design Lakehouse, Data Warehouse, and Medallion Architecture solutions based on business and technical requirements.
• Design and define AI/ML-ready data architectures to support Machine Learning, Generative AI, and advanced analytics use cases.
• Define data architecture patterns for Generative AI, LLM, RAG, embeddings, and vector search solutions
• Evaluate and recommend appropriate technologies, platforms, and architectural patterns based on performance, scalability, security, availability, and cost.
• Create architecture diagrams, data flow diagrams, solution designs, and technical documentation.
• Design secure and scalable integration of enterprise data platforms with AI/ML and Generative AI applications.
• Integrate AI agents with APIs, databases, cloud services (AWS, Azure), and enterprise applications.
• technical and estimate.
• Define enterprise architecture using Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Factory, Dataflows, Power BI, and related Fabric capabilities.
• Design scalable Azure Databricks and Delta Lake architectures for large-scale data processing and analytics.
• Define Bronze, Silver, and Gold data-layer strategies and standards.
• Design Databricks solutions using Apache Spark, Structured Streaming, Delta Lake, Unity Catalog, and optimized data processing patterns.
• Define standards for partitioning, file sizes, schema evolution, incremental processing, data retention, and performance optimization.
• Guide teams in optimizing Databricks workloads for performance and cloud cost efficiency.
• Design data integration architectures for data originating from databases, APIs, files, SaaS applications, Dynamics 365, and streaming sources.
• Define appropriate batch, micro-batch, and real-time/streaming ingestion patterns.
• Establish ETL/ELT patterns using ADF, Fabric Data Factory, Databricks, and other cloud-native services.
• Define frameworks for incremental loads, CDC, SCD Type 1/Type 2, reconciliation, error handling, and data recovery.
• Design reusable and scalable data ingestion frameworks.
• Define conceptual, logical, and physical data models based on business requirements.
• Design dimensional models, star schemas, fact and dimension tables, and analytical data models.
• Define enterprise data standards, naming conventions, data domains, and master/reference data structures.
• Design models optimized for Power BI, analytics, reporting, and downstream applications.
• Define strategies for managing historical data, slowly changing dimensions, data retention, and archival.
• Define enterprise data governance and security architecture across the data platform.
• Implement and govern Microsoft Purview, Unity Catalog, RBAC, data classification, lineage, auditing, and access-control strategies.
• Define security patterns for row-level security, column-level security, masking, encryption, secrets management, and secure data access.
• Establish data ownership, data stewardship, metadata, lineage, and data quality standards.
• Define architecture strategies to support high-volume and high-performance data workloads.
• Identify and resolve architectural bottlenecks across ingestion, storage, transformation, database, and reporting layers.
• Optimize Databricks/Spark, Delta Lake, Fabric, SQL, and Power BI workloads.
• Define appropriate compute sizing, workload isolation, partitioning, caching, indexing, and optimization strategies.
• Perform capacity planning, scalability assessment, and cost optimization for cloud data platforms.
• Design cloud-based data solutions primarily on Microsoft Azure, with knowledge of AWS/GCP data platforms.
• Define integration patterns across Azure, AWS, APIs, SaaS applications, and on-premises systems.
• Evaluate new technologies and recommend solutions based on business requirements and architectural standards.
• Define cloud architecture covering availability, scalability, networking, security, disaster recovery, and operational support.

• Good exposure to work on RFP’s, sales proposal,
• Strong understanding of data governance, security, metadata, lineage, and data quality.
• Experience with cloud architecture, scalability, performance optimization, and cost management.
• Good understanding of CI/CD, DevOps, Infrastructure as Code, monitoring, and deployment strategies.
• Enterprise Data Architecture
• Solution Architecture
• Cloud
• Architecture
• Databricks & Lakehouse Architecture
• Data Modeling
• Data
• Performance
• Cost Optimization
• Technical
• Stakeholder Management