Systems Analyst, Debt

Bellwether•El Segundo, CA

1d•$100,000 - $100,000•Onsite

About The Position

Bellwether is a global advisory firm that helps businesses solve their most complex challenges and enhance enterprise value. We specialize in formulating and executing dynamic growth strategies through true partnership with our clients. Merging deep industry expertise, advanced analytical capabilities and best-in-class talent, we create value across the business lifecycle, from transaction management to operational transformation. Bellwether specializes in the management, analysis, and reporting of over $40 billion of AUM across a wide range of real estate related assets and corporate platforms in North America, Europe and Asia. Bellwether is privately held and has offices in Los Angeles, Newport Beach, Salt Lake City, London and Dallas. Overview The Debt Asset Management Team is responsible for the oversight of a portfolio of commercial real estate debt investments. The Systems Analyst, Debt role is an exciting opportunity to leverage one’s knowledge of technology and business to drive delivery of business initiatives by bridging the gap between business requirements and technical solutions. This role will work closely with business stakeholders to analyze business needs and create scalable solutions. This role will be a key driver in delivering technology-oriented solutions from design to deployment. This role will be responsible for supporting the asset management team in preserving and creating value in the existing portfolio across various property types. The Systems Analyst, Debt will support the team by building technology-oriented solutions, contributing to data strategies, conducting complex financial analyses, authoring quarterly portfolio and asset level reports, and interacting directly with sponsors. In this role there will be tremendous learning and growth opportunities through interactions with leading experts in the real estate investment industry.

Requirements

0-2 years of relevant experience, including internships, research, or early-career work in data engineering, data science, computer science, quantitative analysis, or systems-focused roles.
Strong proficiency in Python building data structures, implementing automation, and creating internal tools for business stakeholders.
Working knowledge of relational databases, including the ability to structure, query, and maintain large datasets.
Demonstrated ability to effectively utilize large language models (LLMs) in a project or business setting, with appropriate controls and human oversight.
Interest in commercial real estate, real estate finance, structured credit, or fixed income investing, with a desire to learn how real estate debt portfolios are underwritten, monitored, and managed over time.
Ability and willingness to learn key real estate debt concepts such as property cash flow, leasing, collateral performance, loan structures, covenant compliance, debt service coverage, maturity risk, and borrower reporting.
Strong communication skills and the ability to work effectively with non-technical professionals, including asset managers and investment professionals.
Demonstrated academic excellence, curiosity, and a genuine interest in using technology to inform decision-making in commercial real estate debt investing and asset management.
Bachelor’s degree in Computer Science, Data Science, Engineering, Applied Mathematics, Information Systems, or another quantitative discipline from a rigorous academic program required.
Strong data modeling and analysis skills with the ability to build complex, multi-scenario property and loan-level models.
Ability to interpret and analyze loan documents, leases, rent rolls, operating statements, and management agreements.
A strong technical background and understanding of data analysis, data modeling, and data systems.
Advanced skills scripting languages such as Python and Structured Query Language (SQL) is encouraged.
Ability to troubleshoot and resolve errors efficiently, strong project management skills, and meticulous attention to detail are critical for success.
Ability to perform responsibilities independently and proactively, solve problems, and improve processes and/or services.
Self-directed, takes initiative and responsibility, ensures completion of tasks.
Excellent verbal and written communication skills; strong listening and interpersonal skills.
Highly effective at preparing and presenting information.
Acts with honesty and integrity and maintains confidentiality, adheres to company policies.
Promotes a collaborative and productive work environment.
Demonstrates team building, support and respect.
Local and/or nationwide travel to assets and client sites required on an as-needed basis, approximately 1-2 times per quarter depending on deal list.
Ability to work independently, and prioritize duties to ensure timely completion.
The ability to collaborate is also imperative.
Requires active listening, critical thinking, making decisions, time management, as well as administration skills.
Ability to interact in a courteous professional manner at all times.
Regular, predictable attendance is required.

Nice To Haves

Exposure to financial, operating, or asset-level data analysis through work experience, internships, project work, or coursework preferred; experience related to real estate, credit, lending, or investment analysis is a plus but not required.

Responsibilities

Build, maintain, and improve data pipelines, implementations, and reporting workflows that support the asset management of commercial real estate debt investments across securitized and non-securitized portfolios, including CMBS, Conduit, SASB, CRE CLO, and other CRE debt structures.
Develop Python and SQL based processes to ingest, clean, and warehouse data from servicing systems, remittance files, borrower reporting, property operating statements, rent rolls, market data, and internal portfolio management tools.
Design data visualizations, reports, and analyses that connect information across the loan, property, tenant, borrower, deal, and bond levels to support surveillance, reporting, valuation, and asset-level decision-making.
Implement workflow automation to improve the team’s ability to oversee large portfolios, identify outliers, and focus asset management attention on credits that require follow-up or deeper review.
Partner closely with asset management professionals to translate real estate debt workflows into scalable tools and solutions.
Utilize large language models (LLMs) to enhance data extraction, coding efficiency, process automation, and internal workflow design, with appropriate controls and human oversight.
Support the loan onboarding and abstraction process, focusing on the abstraction of unstructured data fields from legal documents and underwriting materials so that debt investments can be efficiently monitored over time.
Document process logic, business rules, system architecture, and workflow design so that tools are reliable, maintainable, and aligned with the needs of a real estate debt asset management platform.
Continuously identify opportunities to improve the efficiency, scalability, and analytical depth of the Debt Asset Management Team.