Lead Data Architect - Microsoft Fabric
Data ArchitectData & AI
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Quick Summary
Technical Tools
Data ArchitectData & AI
Requirements
~1 min read- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.
- 10+ years of experience in Data Engineering, Data Warehousing, or Cloud Data Platform development.
- Minimum 8+ years of overall professional experience is mandatory.
- 4+ years of hands-on experience with Microsoft Azure Data Platform technologies.
- At least 2 years of hands-on experience implementing Microsoft Fabric in enterprise environments.
- Strong expertise in SQL, Python, PySpark, and Apache Spark optimisation.
- Hands-on experience with Microsoft Fabric components, including OneLake, Lakehouse, Data Warehouse, Data Factory, Power BI, and Real-Time Intelligence.
- Experience with Azure Data Lake Storage (ADLS), Azure DevOps, GitHub, and CI/CD pipelines.
- Strong understanding of dimensional data modelling, ETL/ELT architecture, and data governance.
- Proven experience designing scalable data platforms and leading enterprise-level data engineering initiatives.
- Microsoft certifications such as DP-600 (Fabric Analytics Engineer), DP-203 (Azure Data Engineer), or Azure Solutions Architect Expert.
- Experience with Power BI semantic model optimisation and DirectLake mode.
- Familiarity with data mesh and domain-driven data architecture principles.
- Experience with Databricks, Azure Synapse Analytics, Azure AI Services, or Microsoft Purview.
- Knowledge of Agile/Scrum delivery methodologies.
- Strong communication, stakeholder management, problem-solving, and technical leadership skills.
Responsibilities
~1 min read- Lead the end-to-end architecture, design, and implementation of enterprise Microsoft Fabric solutions.
- Define technical architecture, engineering standards, reusable frameworks, and best practices for platform adoption.
- Own the architecture of the unified data ecosystem, including OneLake structure, workspace strategy, capacity planning, and domain segregation.
- Establish medallion architecture patterns across Bronze, Silver, and Gold data layers.
- Provide technical leadership to data engineers and developers, ensuring quality, scalability, and consistency.
- Review solution designs, code, and deployment strategies to ensure alignment with architectural standards.
- Design and implement enterprise data platforms using Microsoft Fabric components, including OneLake, Lakehouse, Data Warehouse, Data Factory, Data Engineering, Real-Time Intelligence, and Power BI.
- Architect scalable, cloud-native data solutions supporting analytics, AI, and business intelligence requirements.
- Demonstrate strong understanding of OneLake, Lakehouse and Warehouse architecture, Delta Lake, Parquet formats, and medallion architecture.
- Define workspace and capacity strategies, domain-level segregation, and platform design standards.
- Leverage Fabric capabilities to establish a unified, scalable, and secure enterprise data environment.
- Design and develop robust ETL/ELT pipelines using Microsoft Fabric Data Factory and Spark Notebooks.
- Integrate structured, semi-structured, and unstructured data from cloud-based and on-premises systems.
- Implement data ingestion, transformation, cleansing, validation, and orchestration processes.
- Optimise ingestion strategies to ensure reliability, scalability, and performance.
- Work with SQL, Python, PySpark, and Apache Spark to build and optimise data engineering solutions.
- Design dimensional data models, Lakehouse architectures, and enterprise data warehouses.
- Develop semantic models and datasets to support Power BI reporting and self-service analytics.
- Translate business and analytical requirements into scalable data architecture and engineering solutions.
- Establish data modelling standards and reusable patterns across enterprise workloads.
- Define and implement DevOps practices for Microsoft Fabric, including Git integration, deployment pipelines, and environment promotion across Development, QA, UAT, and Production.
- Build and maintain CI/CD pipelines using Azure DevOps or GitHub Actions.
- Automate deployment of Fabric artefacts, including Lakehouses, Warehouses, Notebooks, Pipelines, and Semantic Models.
- Implement automated testing, validation, release management, and rollback strategies.
- Establish infrastructure-as-code and source control practices for Fabric workloads.
- Architect and enforce enterprise security and access controls across Microsoft Fabric.
- Implement workspace roles, OneLake data access policies, sensitivity labels, and row-level security.
- Work with Microsoft Entra ID, Role-Based Access Control (RBAC), and Microsoft Purview to establish governance frameworks.
- Ensure compliance with enterprise security, privacy, and regulatory requirements.
- Establish consistent data governance and access management practices.
- Optimise Spark workloads, SQL Warehouse performance, and OneLake storage utilisation.
- Implement partitioning, indexing, caching, and workload optimisation strategies.
- Monitor platform performance, identify bottlenecks, and proactively improve system efficiency.
- Ensure scalability, reliability, and efficient resource utilisation across data workloads.
- Lead migration initiatives from legacy data platforms, including Azure Synapse Analytics, Azure Data Factory, on-premises SSIS/SQL Server, and other cloud data warehouses, to Microsoft Fabric.
- Define migration strategies, architecture patterns, and implementation roadmaps.
- Ensure smooth integration, data consistency, and minimal disruption during platform transitions.
- Enable AI, Machine Learning, and Copilot capabilities within Microsoft Fabric.
- Support data science workloads using Spark, notebooks, and machine learning models.
- Collaborate with AI teams to operationalise predictive analytics and intelligent data solutions.
- Leverage AI capabilities to improve engineering productivity and data platform efficiency.
- Lead production deployments and post-deployment support activities.
- Monitor platform health, troubleshoot incidents, and ensure SLA compliance.
- Establish disaster recovery, backup, and business continuity strategies.
- Collaborate with enterprise architects, product owners, business stakeholders, and cross-functional teams.
- Participate in architecture review boards and technical governance meetings.
- Communicate technical solutions, implementation plans, and project status to senior leadership.
- Mentor data engineers and developers on Microsoft Fabric architecture and engineering best practices.
- Conduct code reviews, design reviews, and technical knowledge-sharing sessions.
- Drive continuous improvement initiatives and promote engineering excellence.
- Microsoft Fabric
- Databricks
- PySpark
- SQL
- Azure Data Factory
- ETL/ELT
Location & Eligibility
Where is the job
India
Remote within one country
About this job
- Posted
- 1 day ago
Listing Details
- Posted
- November 13, 2026
- First seen
- November 13, 2026
- Last seen
- November 13, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 68%
- Scored at
- November 14, 2026
Signal breakdown
freshnesssource trustcontent trustemployer trust
External application
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