{"id":3453394,"url":"https://alion.io/job/dataart-lead-microsoft-fabric-data-engineer","title":"Lead Microsoft Fabric Data Engineer","company":{"id":46984,"name":"DataArt","domain":"dataart.com","url":"https://alion.io/company/dataart-com","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"B","score":75,"open_postings":18,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":23,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Orlando, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":130000,"max":140000,"currency":"USD","period":"year","gross":null,"usd_annual":140000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Git","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":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-03T00:00:00Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-31T09:47:45Z","board_verified":true,"closed_at":null,"days_open":58,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":58},"description":"We are looking for an experienced Lead Microsoft Fabric Data Engineer to design and deliver enterprise-scale data and analytics solutions using Microsoft Fabric.\nThis is a deeply hands-on role combining data engineering with architecture ownership. The successful candidate will personally build, optimize, and troubleshoot solutions across OneLake, Lakehouse/Warehouse, Fabric Data Factory, notebooks, SQL/PySpark, and Power BI semantic models, while also defining architectural patterns and engineering standards for the broader team.\nThe role requires someone who can move comfortably between architecture and implementation: making technology and design decisions, validating them through working solutions, resolving production issues, and establishing reusable patterns that other engineers can follow.\nA key focus is the evolution of an existing enterprise BI and data landscape toward Microsoft Fabric while maintaining and improving the performance, reliability, and scalability of current Power BI and analytical workloads.\nThe estimated salary range for this position is USD 130,000 to 140,000 per year. The role requires three days of office work per week in New York/New Jersey, Newark, or Tampa, Florida.\nDesign and implement end-to-end Microsoft Fabric solutions across OneLake, Lakehouse, Warehouse, Data Factory, notebooks, SQL/PySpark, and Power BI.\nBuild and evolve scalable medallion architectures, defining practical patterns for ingestion, transformation, storage, modeling, and consumption.\nDesign and implement batch and incremental data pipelines across relational databases, files, APIs, cloud services, and other enterprise data sources.\nBuild and optimize enterprise Power BI semantic models, including dimensional models, DAX, aggregations, incremental refresh, and appropriate use of Import, DirectQuery, Direct Lake, and composite models.\nDiagnose and resolve performance issues across data pipelines, Delta tables, SQL workloads, semantic models, reports, and Fabric capacity.\nDefine and implement Fabric platform patterns covering workspaces, domains, environments, OneLake data sharing, security, governance, and deployment.\nApply appropriate architectural and engineering patterns for structured, semi-structured, and large-scale analytical data workloads.\nImplement data quality, reconciliation, lineage, metadata, monitoring, and operational controls.\nEstablish and maintain Git-based development, CI/CD, automated testing, deployment, and environment-management practices for Fabric workloads.\nDevelop reusable engineering patterns, accelerators, and reference implementations that can be adopted by other teams.\nSupport and optimize existing enterprise data and BI solutions while identifying appropriate opportunities for incremental modernization and migration to Fabric.\nLead technical assessments and proofs of concept through working implementations and measurable outcomes.\nProvide technical leadership across data engineering, BI, architecture, infrastructure, security, governance, and business teams while remaining a direct contributor to implementation and production support.\nMaintain architecture documentation, data flows, technical standards, and implementation guidance based on the solutions and patterns delivered.\n8+ years of experience in data engineering, data architecture, analytics engineering, or enterprise data-platform delivery, with significant hands-on implementation experience.\nStrong hands-on production experience with Microsoft Fabric, with demonstrated implementation experience across several core workloads such as Lakehouse/Warehouse, Data Factory pipelines, notebooks, SQL/PySpark, and Power BI semantic models.\nDeep practical understanding of OneLake, Fabric Lakehouse, Fabric Data Warehouse, Data Factory, Spark/notebook-based engineering, and Power BI.\nHands-on experience implementing medallion architectures, Delta Lake, Parquet-based storage, and scalable data-processing patterns.\nStrong experience with data ingestion, transformation, orchestration, incremental processing, and large-scale data workloads.\nStrong SQL skills and practical experience with Python and/or PySpark for production data engineering.\nStrong data-modeling fundamentals, including dimensional modeling, star schemas, facts and dimensions, shared/conformed dimensions, and slowly changing dimensions.\nPractical experience building and optimizing enterprise Power BI semantic models, including DAX, Power Query, aggregations, incremental refresh, and performance optimization.\nStrong practical understanding of Direct Lake, Import, DirectQuery, and composite models, including the ability to make storage and connectivity decisions based on data volume, freshness, performance, concurrency, and capacity requirements.\nExperience diagnosing and optimizing performance across SQL, pipelines, data processing, semantic models, Power BI reports, and Fabric workloads.\nExperience working with existing enterprise data and BI environments, not only greenfield implementations, including troubleshooting, optimization, modernization, and incremental migration.\nExperience integrating relational databases, files, APIs, cloud services, and other enterprise data sources into modern analytical platforms.\nStrong practical knowledge of security, access control, governance, lineage, and data protection, including Entra ID, RBAC, RLS/OLS, managed identities, and related enterprise patterns.\nExperience implementing data quality, reconciliation, observability, monitoring, and operational reliability practices.\nPractical understanding of Fabric capacity management and performance, including workload monitoring, capacity constraints, query optimization, and cost/performance considerations.\nExperience with Git-based development, CI/CD, automated testing, and environment management for data workloads.\nAbility to translate complex business and technical requirements into working solutions and take ownership from design through implementation and production support.\nAbility to provide technical leadership and make architecture decisions while remaining a strong hands-on contributor.\nStrong communication skills and ability to collaborate effectively with engineering, architecture, BI, infrastructure, security, governance, and business stakeholders.\nBachelor's degree in Computer Science, Data Engineering, Information Systems, or equivalent practical experience.\nAvailability to visit the client office 2-3 times per week.\nMicrosoft Fabric certification (DP-600 and/or DP-700) strongly preferred.\nRelevant Power BI, Azure Data, or architecture certifications.\nExperience working with investment management, asset management, fixed income, financial products, risk, compliance, or distribution data.\nHands-on experience with Microsoft Purview, Azure Data Lake Storage, Azure Data Factory, Azure Synapse Analytics, Azure Key Vault, and Azure Monitor.\nExperience integrating Microsoft Fabric with SQL Server, Oracle, Snowflake, Databricks, or other enterprise data platforms.\nExperience modernizing or migrating legacy data warehouses, Power BI solutions, Azure Synapse, Azure Data Factory, or other enterprise analytical workloads to Microsoft Fabric.\nKnowledge of data mesh, domain-oriented data architecture, and federated governance applied in enterprise environments.\nExperience with Fabric Real-Time Intelligence, event streaming, or operational analytics.\nFamiliarity with TMDL, PBIP, Tabular Editor, XMLA endpoints, or semantic-model automation.\nExposure to Microsoft AI Foundry, GenAI, agents, or AI-enabled analytics.","description_format":"text","description_chars":7581,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Outsourcing & Dedicated Teams","Custom Software 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