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Mid-level data engineering role, metro location, and generalist title increase applicant competition.
Data engineering skills transferable across industries, but Fabric/Azure specificity moderately limits portability.
Explicit 3–6 years and mandatory Fabric, PySpark, and Azure skills enforce strict technical filters.
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Build and optimize ingestion pipelines and data transformation workflows using Microsoft Fabric and Spark/PySpark for financial reporting.
Develop and manage Lakehouse/Warehouse data structures and implement data quality frameworks to meet data readiness SLAs.
Collaborate with application teams for integration and monitoring of Fabric capacity and performance to support application modernization.
3-6 years of experience in data engineering or related roles.
Strong expertise in Microsoft Fabric including Lakehouse architecture, pipelines, Notebooks (Spark), Dataflows Gen2, and capacity management.
Proficient in PySpark, SQL, data modeling, ETL/ELT design, and data validation frameworks.
Experience with Azure integrations such as Event Hub, Service Bus, and Managed Identity.
Experienced in building and optimizing data pipelines in Microsoft Fabric with a strong focus on financial data workflows.
Comfortable working at the intersection of data engineering and application integration within a cloud environment (Azure).
Able to design and maintain scalable data quality and reconciliation frameworks to ensure SLA compliance in a production environment.