Capital Markets Data Analyst
New
United StatesFull-TimeMiddle
Salary95,000 - 135,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- SQLETLMicrosoft Power BIData modelingData analytics
Requirements
- 5+ years of experience in business intelligence, data analytics, or a similar data-focused role.
- Advanced SQL expertise with proven ability to analyze, debug, and improve complex existing data structures independently.
- Strong analytical mindset with a critical approach to data validation, reconciliation, and root cause analysis.
- Experience working with ETL processes and maintaining data pipelines in production environments.
- Hands-on Power BI experience, including dashboard development and semantic data model design.
- Ability to work independently while managing priorities and collaborating with technical and business stakeholders.
- Experience with Microsoft Fabric (Lakehouse, Warehouse, Notebooks) and Azure SQL is highly desirable.
- Exposure to mortgage banking or capital markets concepts such as Gain on Sale, hedging, or investor reporting is a strong plus.
Responsibilities
- Develop, optimize, and maintain complex SQL queries, stored procedures, and data models to ensure accuracy and performance of capital markets datasets.
- Investigate and resolve data discrepancies, reconciliation issues, and reporting variances by tracing root causes across data pipelines.
- Maintain and monitor ETL processes and data workflows in Microsoft Fabric and Azure Data Factory to ensure reliability and data freshness.
- Build and enhance Power BI dashboards and semantic models supporting P&L reporting, investor exposure, and capital markets performance metrics.
- Partner directly with capital markets stakeholders to translate business questions into robust, reusable, and scalable data solutions.
- Support investor data integration efforts, ensuring accurate mapping and maintenance of key trading and program data elements.
- Participate in process improvement and automation initiatives to reduce manual effort and improve data quality and efficiency.
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