Backend Engineer - 64624178818

Location: Remote - LATAM 

Compensation: $3,000 – $4,000 USD / month (commensurate with senior execution capability)

Type: Full-Time, (Full U.S. Business Hours Alignment)

Our client is scaling its tech infrastructure, and we are looking for a backend-focused engineer to build the core data and integration layer of our lending business.

Currently, our operational data lives across spreadsheets and disconnected platforms. You will lead the transition off spreadsheets by building a reliable backend powered by well-designed relational databases, robust Python services, and automated API integrations.

Working directly with our Head of Technology Transformation, you will have direct access to leadership and meaningful influence over architecture, engineering practices, and the future of our data platform.

• SQL Server Database Layer: Design, build, and own our SQL Server databases, including schemas, views, stored procedures, migrations, and query layers utilized by internal tools, Power BI reporting, and AI agents.
• API Integrations & Automation: Develop and maintain webhooks, REST APIs, and scheduled background processes to automate workflows and synchronize data across core platforms.
• ETL/ELT Pipelines: Design pipelines that ingest, clean, transform, validate, and move data from legacy spreadsheets into our central SQL database.
• Data Modeling & Architecture: Design efficient, clean data models that support lending operations, risk analytics, and real-time business reporting.
• Data Quality & Reliability: Implement automated testing, monitoring, and validation controls to ensure high data integrity across all environments.

• Cloud Infrastructure (Azure): Support infrastructure-as-code deployments and CI/CD workflows on Azure.
• Security & Access Control: Configure secure application connectivity, secrets management, authentication, and least-privilege access protocols.
• Analytics & Visualization: Build dashboards and reports in Power BI to give operational visibility to executive leadership.
• NoSQL Exploration: Extend into document/NoSQL storage where appropriate for unstructured datasets.

• Experience: 3–4+ years of hands-on backend or data engineering experience building production systems.
• Core Tech Stack: Advanced SQL (data modeling, query optimization, indexing) + Strong Python (data manipulation, script execution, API consumption).
• Integration Expertise: Proven experience building, maintaining, and troubleshooting API integrations between disparate business systems.
• AI-Native Engineering: Strong interest and active workflow integration with modern AI engineering tools (ChatGPT, Claude, Perplexity, Cursor) to multiply output and speed.
• Cloud & Security Fundamentals: Working knowledge of Azure networking (VNets, private endpoints, firewalls) and security standards (secrets management, data protection).
• Software Engineering Discipline: Strict adherence to Git best practices, code reviews, automated testing, and clean technical documentation.
• Communication: Exceptional written and spoken English, strong self-direction, and a reliable remote setup for daily cross-functional collaboration.

• Prior experience in Lending, Real Estate, Banking, or Financial Services / Fintech.
• Infrastructure-as-code tools (Terraform).
• Automation using VBA or PowerShell to extract legacy data.
• CI/CD automation via GitHub Actions or Azure DevOps.
• Power BI modeling and dashboard design.

• Broad & Adaptable: Happy switching between database design, Python scripts, and Azure infrastructure in the same afternoon.
• Comfortable with Ambiguity: Ability to take loose operational requirements and turn them into a working, testable MVP quickly.
• Proactive Communicator: Highlights risks or uncertainties early, asks sharp questions, and takes pride in clean execution.

Please submit your resume along with brief responses to the following:

1. What is the most interesting process you’ve automated, and what legacy system did it replace?
2. Describe an integration you built between two systems that was harder than expected. What broke, and how did you resolve it?
3. Share an example of a database schema you designed in the past—what would you do differently if you built it again today?