Job Description
Azure Fabric Engineer Cyberlocke, LLC McKinney, TX Job Details Full-time $100,000 - $125,000 a year 1 day ago Qualifications Data Integration (Data management) SQL databases Data migration IT system monitoring Application deployment Automating deployment processes SQL Azure DevOps proficiency Access control implementation Software documentation Technical writing Technical troubleshooting support Cross-functional collaboration Cross-functional communication Full Job Description We are seeking an experienced Azure Fabric Engineer to design, build, and operationalize modern data solutions across Microsoft Fabric and Azure. This role will bridge data architecture, ETL/ELT pipeline development, data migration, and DevOps/DataOps practices to create scalable, secure, and production-ready data platforms. The ideal candidate understands how to architect a governed data environment while also being hands-on in building pipelines, supporting deployments, integrating disparate source systems, and improving operational reliability across development, test, and production environments. Key Responsibilities Data Platform Architecture & Design Design scalable, secure, and high-performing data platforms using Microsoft Fabric , Azure Data Services , Lakehouse , Warehouse , and OneLake . Define data architectures that support ingestion, transformation, storage, analytics, and operational reporting. Consolidate structured, semi-structured, and unstructured data from multiple systems into a unified data platform. Partner with business stakeholders, analysts, engineers, and technical teams to translate business requirements into practical technical solutions. ETL/ELT Engineering & Data Migration Design, develop, and maintain ETL/ELT pipelines for ingesting, transforming, and loading data into Microsoft Fabric and Azure-based environments. Lead or support data migration initiatives from legacy, on-premises, and third-party platforms into modern Azure/Fabric architectures. Perform source-to-target mapping, transformation logic development, reconciliation, and migration validation. Build repeatable ingestion patterns for databases, flat files, APIs, SaaS platforms, and cloud storage sources. Optimize pipelines for performance, reliability, scalability, and cost efficiency. DevOps / DataOps / Deployment Enablement Design and implement CI/CD pipelines for Fabric artifacts and supporting data workloads across dev, test, and production environments.
Standardize deployment practices including:
environment parameterization secret management service principal-based deployments release controls and approval workflows Support automation using Azure DevOps , Git-based workflows, YAML pipelines, and Fabric/API-based deployment methods. Establish deployment, rollback, and recovery procedures for production data solutions. Build orchestration and execution frameworks that support scheduling, dependency handling, retries, restartability, and backfills. Governance, Security & Operational Readiness Help define and implement governance controls including metadata, lineage, access control, auditability, and data quality validation. Support compliance and security best practices aligned to organizational and regulatory requirements. Implement monitoring and alerting for pipeline failures, data quality issues, and data freshness SLAs. Define and document operational procedures for incident response, reruns, escalation, support, and daily monitoring. Contribute to RTO/RPO planning, operational resilience, and production support readiness. Documentation, Collaboration & Leadership Produce and maintain architecture diagrams, technical documentation, runbooks, SOPs, and deployment guides. Conduct knowledge transfer and support operational enablement for engineering and support teams. Provide technical leadership and contribute best practices for Microsoft Fabric adoption, pipeline engineering, and deployment standards. Mentor junior engineers and participate in design reviews, code reviews, and technical planning sessions. Required Qualifications Bachelor's degree in Computer Science, Information Systems, Engineering, or related field, or equivalent practical experience. 4+ years of experience in data engineering, data platform engineering, or cloud data solutions . Hands-on experience with Microsoft Fabric and core Azure data services. Strong experience building and supporting ETL/ELT pipelines and data migration solutions . Experience with Azure DevOps , Git workflows, CI/CD pipelines, and release management practices. Strong SQL skills and working knowledge of Spark , PySpark , or related data engineering tools. Experience with Lakehouse , Warehouse , Delta/Parquet-style processing , and cloud data storage concepts. Understanding of monitoring, orchestration, restartability, and production support requirements for data platforms. Experience with service principals, RBAC, secret management, and secure deployment practices. Strong documentation, troubleshooting, and cross-functional collaboration skills. Preferred Qualifications Experience with Azure Synapse , Azure Data Factory , ADLS Gen2 , and OneLake . Familiarity with Power BI semantic models , Direct Lake, and downstream analytics integration. Experience with data governance , lineage, metadata, and compliance-oriented data controls. Knowledge of Fabric REST APIs, automation frameworks, and enterprise scheduling tools. Experience defining operational standards, runbooks, and support models for enterprise data platforms. Microsoft Azure or Microsoft Fabric certifications. Core Technical Skills Microsoft Fabric Azure DevOps Azure Data Factory Azure Synapse Lakehouse / Warehouse OneLake / ADLS Gen2 SQL Spark / PySpark ETL / ELT
Data Migration CI/CD and Release Management DataOps / Orchestration REST API
Automation RBAC / Entra ID / Key Vault Ideal Profile This role is ideal for someone who can operate between solution architecture and hands-on engineering . The right candidate is comfortable designing the bigger picture, but also enjoys building pipelines, solving deployment challenges, improving operational maturity, and making data platforms production-ready.
