Microsoft Fabric Technical Lead

Role : Microsoft Fabric Technical Lead Duration : Long Term Contact Location : Hartford CT Hybrid Feedback from the Manager We are receiving candidates who are either primarily experienced in Databricks or Snowflake, but their profiles are being repurposed to reflect MS Fabric experience. Another common scenario is candidates with experience limited to Power BI, which is only one component of MS Fabric. However, the requirement is for a Data Engineer who can build and manage the end-to-end data pipeline, as outlined in the first line of the JD. Job Description: Data Technical Lead — Microsoft Fabric Lead end‑to‑end delivery on Microsoft Fabric (OneLake, Lakehouse, Data Pipelines, Synapse in Fabric, Power BI), setting reference patterns and engineering standards. Drive performance optimization across lakehouse and BI layers: star‑schema modeling, DAX/Power BI model tuning, query folding, incremental refresh, partitioning/caching, and load testing to meet SLA/SLO targets. Own NFR management & realization (scalability, reliability, security/compliance, cost/FinOps, data quality): define SLOs/capacity plans; implement monitoring/alerting (Fabric monitoring, Log Analytics) and resilience testing. Architect Domina and data products delivered through medallion architecture; should have implementation awareness of Data Governance, Lineage and Data Cataloging; champion CI/CD (Fabric Git integration), environment strategy, and release governance. Provide technical leadership to squads (design/code reviews, solution spikes, performance deep‑dives), and remove delivery blockers in collaboration with CloudOps/InfoSec/Networking. Translate business goals into technical roadmaps; manage risks, dependencies, milestones; communicate progress to senior stakeholders. Nice to have - P&C insurance domain fluency (policy/claims/billing data, loss triangles, regulatory/market conduct reporting) to accelerate solution design. Enterprise data strategy & program management (road mapping, backlog/prioritization, estimation/budget, vendor management); apply data mesh/medallion patterns and align with enterprise architecture.