Sr. Data Analyst, Collateral Data Solutions
United StatesFull-TimeSenior
Salary120,000 - 130,000 USD per year
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Job Details
- Experience
- 5+ years of experience working with financial data, ideally within structured finance, capital markets, or loan-level analytics.
- Required Skills
- PythonSQLR
Requirements
- 5+ years of experience working with financial data, ideally within structured finance, capital markets, or loan-level analytics.
- Strong understanding of loan origination, servicing data, and securitization structures (ABS, RMBS, or related asset classes preferred).
- Proficiency in SQL, Python, or R for data analysis and manipulation of large datasets.
- Experience working with complex financial statements, investor reports, and trustee reporting.
- Strong analytical mindset with ability to extract insights from raw and unstructured financial data.
- Detail-oriented with the ability to manage multiple analytical workstreams simultaneously.
- Strong communication skills with experience in client-facing or stakeholder-facing roles.
- Ability to bridge technical data work with business and financial interpretation.
- Curiosity and adaptability to work in a fast-paced, evolving data and finance environment.
Responsibilities
- Analyze large-scale loan-level and structured finance datasets to extract meaningful insights and support investment decision-making.
- Work with securitization data sources, including trustee reports, deal documents, and servicing files, to build accurate analytical models.
- Develop and refine SQL, Python, or R-based workflows to process and analyze complex financial datasets.
- Collaborate with engineering and product teams to design, build, and operationalize scalable data solutions and analytics tools.
- Translate raw financial data into clear metrics, dashboards, and reports used by institutional clients.
- Engage directly with clients, including hedge funds, banks, and asset originators, to deliver reporting and analytical support.
- Identify trends, risks, and performance drivers across multiple asset classes and structured products.
- Ensure accuracy, consistency, and integrity of financial datasets used for client-facing and internal decision-making.
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