{"id":4249901,"url":"https://alion.io/job/weekday-lead-data-architect-fabric","title":"Lead Data Architect (Fabric)","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":80,"open_postings":117,"ghost_share":0,"stale_share":0.983,"repost_share":0,"time_to_fill_p50_days":5,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":{"min":2000000,"max":6000000,"currency":"INR","period":"year","gross":null,"usd_annual":64062},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Entra ID","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"SSIS","optional":false},{"name":"Agile","optional":true},{"name":"Scrum","optional":true}],"status":"live","first_seen_at":"2026-11-13T13:53:58Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T13:53:58Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"This role is for one of Weekday’s clients\nSalary range: Rs 2000000 - Rs 6000000 (ie INR 20 - 60 LPA)\nExperience: 10+ Years Overall (Minimum 8+ Years Required)\nMicrosoft Fabric Experience: 2+ Years of Hands-on Experience\nLocation: Remote - Anywhere in India\nEmployment Type: Full-Time\nRole Overview\nWe are looking for an experienced Lead Data Architect - Microsoft Fabric to design, build, and operationalise scalable enterprise data platforms on Microsoft Azure. This is a senior, hands-on architecture role requiring strong expertise in data engineering, cloud data platforms, DevOps, and Microsoft Fabric.\nThe selected candidate will own the architecture of a unified enterprise data ecosystem spanning OneLake, Data Factory, Synapse Data Engineering, Power BI, and Real-Time Analytics. The role involves defining technical direction, establishing engineering standards, implementing CI/CD and automation frameworks, and ensuring platform security, governance, scalability, and operational reliability.\nYou will work closely with data engineers, analysts, enterprise architects, product owners, and business stakeholders to deliver modern data solutions that support analytics, artificial intelligence, machine learning, and business intelligence workloads.\nImportant: This is a senior-level position. Candidates must have at least 8 years of overall professional experience, with 10+ years preferred, and relevant hands-on experience with Microsoft Fabric or equivalent Azure data platform technologies.\nRequirements\nKey Responsibilities\n1. Enterprise Architecture and Technical Leadership\nLead the end-to-end architecture, design, and implementation of enterprise Microsoft Fabric solutions.\nDefine technical architecture, engineering standards, reusable frameworks, and best practices for platform adoption.\nOwn the architecture of the unified data ecosystem, including OneLake structure, workspace strategy, capacity planning, and domain segregation.\nEstablish medallion architecture patterns across Bronze, Silver, and Gold data layers.\nProvide technical leadership to data engineers and developers, ensuring quality, scalability, and consistency.\nReview solution designs, code, and deployment strategies to ensure alignment with architectural standards.\n2. Microsoft Fabric Platform Architecture\nDesign and implement enterprise data platforms using Microsoft Fabric components, including OneLake, Lakehouse, Data Warehouse, Data Factory, Data Engineering, Real-Time Intelligence, and Power BI.\nArchitect scalable, cloud-native data solutions supporting analytics, AI, and business intelligence requirements.\nDemonstrate strong understanding of OneLake, Lakehouse and Warehouse architecture, Delta Lake, Parquet formats, and medallion architecture.\nDefine workspace and capacity strategies, domain-level segregation, and platform design standards.\nLeverage Fabric capabilities to establish a unified, scalable, and secure enterprise data environment.\n3. Data Engineering and Integration\nDesign and develop robust ETL/ELT pipelines using Microsoft Fabric Data Factory and Spark Notebooks.\nIntegrate structured, semi-structured, and unstructured data from cloud-based and on-premises systems.\nImplement data ingestion, transformation, cleansing, validation, and orchestration processes.\nOptimise ingestion strategies to ensure reliability, scalability, and performance.\nWork with SQL, Python, PySpark, and Apache Spark to build and optimise data engineering solutions.\n4. Data Modelling and Analytics\nDesign dimensional data models, Lakehouse architectures, and enterprise data warehouses.\nDevelop semantic models and datasets to support Power BI reporting and self-service analytics.\nTranslate business and analytical requirements into scalable data architecture and engineering solutions.\nEstablish data modelling standards and reusable patterns across enterprise workloads.\n5. DevOps, CI/CD and Automation\nDefine and implement DevOps practices for Microsoft Fabric, including Git integration, deployment pipelines, and environment promotion across Development, QA, UAT, and Production.\nBuild and maintain CI/CD pipelines using Azure DevOps or GitHub Actions.\nAutomate deployment of Fabric artefacts, including Lakehouses, Warehouses, Notebooks, Pipelines, and Semantic Models.\nImplement automated testing, validation, release management, and rollback strategies.\nEstablish infrastructure-as-code and source control practices for Fabric workloads.\n6. Security, Governance and Compliance\nArchitect and enforce enterprise security and access controls across Microsoft Fabric.\nImplement workspace roles, OneLake data access policies, sensitivity labels, and row-level security.\nWork with Microsoft Entra ID, Role-Based Access Control (RBAC), and Microsoft Purview to establish governance frameworks.\nEnsure compliance with enterprise security, privacy, and regulatory requirements.\nEstablish consistent data governance and access management practices.\n7. Performance Optimisation\nOptimise Spark workloads, SQL Warehouse performance, and OneLake storage utilisation.\nImplement partitioning, indexing, caching, and workload optimisation strategies.\nMonitor platform performance, identify bottlenecks, and proactively improve system efficiency.\nEnsure scalability, reliability, and efficient resource utilisation across data workloads.\n8. Cloud Migration and Platform Modernisation\nLead 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.\nDefine migration strategies, architecture patterns, and implementation roadmaps.\nEnsure smooth integration, data consistency, and minimal disruption during platform transitions.\n9. AI and Advanced Analytics\nEnable AI, Machine Learning, and Copilot capabilities within Microsoft Fabric.\nSupport data science workloads using Spark, notebooks, and machine learning models.\nCollaborate with AI teams to operationalise predictive analytics and intelligent data solutions.\nLeverage AI capabilities to improve engineering productivity and data platform efficiency.\n10. Production Support and Stakeholder Management\nLead production deployments and post-deployment support activities.\nMonitor platform health, troubleshoot incidents, and ensure SLA compliance.\nEstablish disaster recovery, backup, and business continuity strategies.\nCollaborate with enterprise architects, product owners, business stakeholders, and cross-functional teams.\nParticipate in architecture review boards and technical governance meetings.\nCommunicate technical solutions, implementation plans, and project status to senior leadership.\n11. Team Leadership and Mentoring\nMentor data engineers and developers on Microsoft Fabric architecture and engineering best practices.\nConduct code reviews, design reviews, and technical knowledge-sharing sessions.\nDrive continuous improvement initiatives and promote engineering excellence.\nRequired Qualifications and Skills\nBachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.\n10+ years of experience in Data Engineering, Data Warehousing, or Cloud Data Platform development.\nMinimum 8+ years of overall professional experience is mandatory.\n4+ years of hands-on experience with Microsoft Azure Data Platform technologies.\nAt least 2 years of hands-on experience implementing Microsoft Fabric in enterprise environments.\nStrong expertise in SQL, Python, PySpark, and Apache Spark optimisation.\nHands-on experience with Microsoft Fabric components, including OneLake, Lakehouse, Data Warehouse, Data Factory, Power BI, and Real-Time Intelligence.\nExperience with Azure Data Lake Storage (ADLS), Azure DevOps, GitHub, and CI/CD pipelines.\nStrong understanding of dimensional data modelling, ETL/ELT architecture, and data governance.\nProven experience designing scalable data platforms and leading enterprise-level data engineering initiatives.\nMust-Have Skills\nMicrosoft Fabric\nDatabricks\nPySpark\nGood-to-Have Skills\nSQL\nAzure Data Factory\nETL/ELT\nPreferred Qualifications\nMicrosoft certifications such as DP-600 (Fabric Analytics Engineer), DP-203 (Azure Data Engineer), or Azure Solutions Architect Expert.\nExperience with Power BI semantic model optimisation and DirectLake mode.\nFamiliarity with data mesh and domain-driven data architecture principles.\nExperience with Databricks, Azure Synapse Analytics, Azure AI Services, or Microsoft Purview.\nKnowledge of Agile/Scrum delivery methodologies.\nStrong communication, stakeholder management, problem-solving, and technical leadership 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