Data and Analytics Engineer

Key Responsibilities

1. Data Engineering & Analytics

• Financial & Real Estate Modeling: Build and maintain advanced underwriting models, cash flow analyses, and fund/portfolio models in Excel (VBA, Power Query), SQL, and Python.
• Data Management: Develop robust data pipelines, ETLs, and integrations in Microsoft Azure and SQL Server for loan, asset, portfolio, and fund-level datasets.
• Analytics & Visualization: Create and manage Tableau dashboards and Excel-based reporting tools to deliver real-time insights on CRE assets, portfolio performance, and credit risk.
• Data Governance & Security: Implement best practices for data quality, security, and compliance, ensuring integrity and reliability of financial and operational data.

2. Workflow Automation & Application Development

• Automation Solutions: Design automated processes for underwriting, portfolio/fund modeling, and risk assessment to reduce manual tasks and improve accuracy.
• Low-Code/AI Platforms: Build internal web apps and APIs using Python, JSON, Power Apps, n8n, and other low-code/AI frameworks.
• Systems Integration: Integrate data from third-party CRE platforms, loan servicing systems, CRMs, and fund accounting tools to consolidate financial modeling and reporting.

3. DevOps & Infrastructure

• CI/CD Pipelines: Implement and maintain continuous integration and deployment workflows (Azure DevOps, GitHub Actions) for consistent, test-driven releases.
• Containerization & Scalability: Leverage Docker/Kubernetes for scalable, secure deployment of analytics applications and data services.
• Infrastructure Automation: Use Terraform/ARM templates or similar tools to provision and manage Azure resources efficiently.
• DevSecOps: Uphold rigorous data security standards, including encryption, access control, and compliance protocols for sensitive financial data.

4. Cross-Functional Collaboration

• Stakeholder Engagement: Partner with underwriters, asset/fund managers, and risk teams to translate CRE and fund-modeling requirements into technical solutions.
• Data-Driven Insights: Present complex analytics, forecasts, and recommendations on underwriting decisions, portfolio performance, fund returns, and credit risk to business leaders.
• Innovation: Stay informed on emerging AI, ML, and automation trends that can enhance fund modeling, real estate analytics, and operational efficiency.

Qualifications

Technical Expertise

• Real Estate & Finance: Deep familiarity with CRE lending, fund/portfolio modeling, and advanced financial modeling practices.
• Data & Analytics: Expert-level proficiency in MS Azure (Data Factory, SQL DB), SQL, Tableau, Excel (VBA, Power Query), ETLs, and data modeling.
• Programming: Demonstrated experience in Python, JSON, JavaScript, Git/GitHub, VS Code, and low-code AI frameworks for application development.
• DevOps: Hands-on experience with CI/CD, containerization (Docker/Kubernetes), and infrastructure-as-code (Terraform/ARM templates).

Soft Skills & Business Acumen

• Ability to interpret complex CRE and fund modeling requirements, aligning technical solutions with financial strategies and performance objectives.
• Strong communication skills for explaining technical concepts to cross-functional teams (finance, credit, operations).
• Self-starter mindset with an aptitude for managing multiple projects in a fast-paced environment.

Preferred Experience

• Working knowledge of CRE loan servicing systems, underwriting software, or asset/fund management platforms.
• Background in credit risk modeling, fund performance analysis, or structured finance.
• Familiarity with CRM automation, alternative lending technologies, and advanced CRE data sources.