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Mid-level, remote-friendly role with broad Azure/MLOps skillset attracts many qualified applicants.
Requires specific Microsoft Fabric/Azure MLOps platform experience, limiting cross-industry transferability.
Multiple mandatory Azure/Fabric, CI/CD, and MLOps platform requirements enforce rigid technical screening.
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Build and operate Microsoft Fabric data and machine learning platforms enabling enterprise production ML workloads with environment governance, CI/CD, and monitoring.
Own end-to-end technical delivery of platform workstreams including workspace structures, data pipelines, scheduling, data quality gates, and security compliance.
Produce operational documentation and enable client teams to manage and operate platforms post-handoff, ensuring unattended, reproducible production runs.
5+ years experience building and operating production data platforms on Microsoft Azure.
Hands-on expertise with Microsoft Fabric (workspaces, OneLake, Lakehouse, Warehouses, Notebooks, RBAC, deployment pipelines) and strong Python and SQL programming skills for production-grade code.
Experience with CI/CD automation for data and notebook assets via Azure DevOps or GitHub Actions and orchestration tools such as Fabric Data Pipelines, Azure Data Factory, or Airflow.
Bachelor's degree in Computer Science, Data Science, Engineering, or related field; Minimum four hours daily overlap with US Eastern business hours.
Experienced in operationalizing ML models into scheduled production environments, including model versioning, batch scoring, failure handling, and monitoring model health/drift.
Strong understanding of Azure security and identity (Entra ID, managed identities, service principals, Key Vault) enabling unattended headless scheduled workloads.
Familiar with integrating platform outputs with Power BI and comfortable producing clear documentation and runbooks for client IT and data science teams.