Imagine being part of a team that is actively driving the future. At Davies, we combine technology, analytics, and professional services to help clients solve complex business challenges.
We are seeking a Security Engineer with Microsoft Fabric and Azure experience to design, implement, and govern secure data platform access, identity controls, row-level security, and compliance-ready security patterns.
We are seeking a Security Engineer with Microsoft Fabric and Azure experience to design, implement, and govern secure data platform access, identity controls, row-level security, and compliance-ready security patterns.
- Design and implement security controls across Microsoft Fabric, Azure, Power BI, warehouses, lakehouses, semantic models, and analytics services.
- Develop row-level security, object-level security, workspace access models, role assignments, and least-privilege patterns using Microsoft Entra ID groups.
- Partner with engineering, analytics, infrastructure, and governance teams to secure pipelines, reporting assets, AI solutions, and client-facing analytics.
- Perform security reviews, access audits, permission testing, data classification validation, and risk remediation across Fabric and Azure.
- Create reusable security standards, implementation guides, monitoring practices, and documentation for scalable platform operations.
- Bachelor's degree in Cybersecurity, Computer Science, Information Systems, Data Engineering, or a related field.
- 4+ years of experience in cloud security, data platform security, identity and access management, or Azure security engineering.
- Hands-on experience securing Microsoft Fabric, Power BI, Azure Data Lake, Azure Synapse, Azure SQL, Azure Storage, or similar analytics platforms.
- Strong understanding of RLS, role-based access control, Microsoft Entra ID, service principals, managed identities, networking, encryption, auditing, and DLP.
- Ability to translate security requirements into practical controls, test plans, documentation, governance standards, and business-friendly recommendations.