Dynamics Data Engineer
NO VISA CANDIDATES / NO AGENCIES / NO THIRD PARTIESSenior Data Engineer (Microsoft Fabric, Dataverse & AI Data Platforms)Position SummaryWe are seeking a highly skilled Data Engineer to design, build, and govern enterprise data platforms that support operational applications, business analytics, and AI initiatives. This role will develop scalable data architectures using Microsoft Fabric tools, SQL Server, Dataverse, and Azure services to deliver trusted, AI-ready data assets across the organization.The ideal candidate combines expertise in data engineering, dimensional and operational modeling, application integration, analytics enablement, and modern AI data architectures. This individual will partner closely with business stakeholders, application teams, architects, analysts, and AI initiatives to create reusable, governed, and scalable data products.Key ResponsibilitiesData Platform EngineeringDesign and implement enterprise data architectures using Microsoft Fabric, OneLake, Lakehouse, Warehouse, and SQL Server technologies.Build and maintain scalable ingestion frameworks from:DataverseSQL ServerREST APIsSaaS applicationsFlat files and SFTP sourcesDevelop Fabric Data Pipelines, Dataflows Gen2, Notebooks, and Semantic Models.Implement bronze, silver, and gold data layer architectures.Optimize data pipelines for performance, reliability, scalability, and cost management.Application Data ModelingDesign Dataverse and operational application data models.Support application solution teams with:Data architectureRelationship designMaster data managementIntegration patternsEstablish reusable canonical business entities across systems.Design APIs and data contracts that support application interoperability.Partner with development teams to ensure applications generate high-quality analytical and AI-ready data.Analytics Data ModelingDesign dimensional, star-schema, and semantic models.Create business-friendly analytical structures supporting Power BI and self-service analytics.Build conformed dimensions and enterprise metrics.Develop data marts and analytical products that drive reporting consistency.Support Data Lake and enterprise semantic model strategies.AI Data EngineeringDesign and prepare AI-ready datasets.Curate structured, semi-structured, and unstructured data assets for AI use cases.Build data pipelines that support:Retrieval-Augmented Generation (RAG)AI agentsSemantic searchMachine learning initiativesImplement metadata, lineage, and semantic enrichment patterns to improve AI effectiveness.Support AI model training, evaluation, and operationalization workflows.Data Governance & QualityImplement data quality monitoring and remediation processes.Support enterprise data governance initiatives.Define and maintain:Data lineageMetadataData catalogingData ownershipData stewardship processesEnsure compliance with security, privacy, and regulatory standards.Integration EngineeringDesign and implement integrations using:DataverseFabricPower PlatformAzure FunctionsLogic AppsREST APIsEvent-driven architecturesDevelop near real-time and batch integration patterns.Support change data capture (CDC) and data synchronization solutions.Performance & Operational ExcellenceMonitor and optimize Fabric workloads.Tune SQL Server databases, queries, and data models.Establish DevOps and CI/CD practices for data assets.Develop monitoring, alerting, and observability processes.Support production operations and incident resolution.Required SkillsAdvancedMicrosoft FabricSQL ServerT-SQLDataversePower BI Semantic ModelsData ModelingETL / ELT DesignData WarehousingLakehouse ArchitectureStrongPythonSpark / PySparkFabric NotebooksData PipelinesDataflows Gen2REST APIsAzure FunctionsPower PlatformKnowledge OfMedallion ArchitectureDelta TablesOneLakeMaster Data ManagementMicrosoft PurviewAI / LLM ArchitecturesRAG PatternsData GovernancePreferred ExperienceMicrosoft Fabric implementation experience.Dataverse data architecture and integration experience.Healthcare, pharmacy, financial, or operational analytics experience.Building AI-ready data platforms.Designing enterprise-scale semantic and dimensional models.Working with both operational application data and analytical reporting environments.