Case Study
Real Estate Investment Data Pipeline (Next.js + Python)
Full-Stack Analytics Dashboard for Brickbro — transforming raw property data into actionable insights using Airflow ETLs and Atomic Design.
- ETL
- Pipeline aggregating market, property, and geospatial data sources
- Dual
- Database persistence — MongoDB (documents) + PostgreSQL (relational)
- Maps
- Google Maps API integration for geospatial property context
- Atomic
- Design system applied to Next.js frontend reporting dashboard
Overview
What was built
Brickbro Property Analysis is a commercial real estate evaluation system built to help investors make informed decisions using structured data and analytical reporting. The platform aggregates property-level metrics, market indicators, and geospatial data to generate structured insights and practical investment recommendations.
As a full-stack developer working closely with engineers and designers, I contributed to backend services, data evaluation logic, and frontend reporting interfaces that transformed raw property data into meaningful decision-support outputs.
The system addresses a core pain point in commercial property investment: data is fragmented across multiple sources, manual assessment is time-consuming, and there is no consistent analytical reporting layer. The platform consolidates these inputs into a structured evaluation pipeline and surfaces results through a clean reporting interface.
System Architecture
Data Pipeline & Evaluation Flow
End-to-end flow from raw data ingestion through ETL processing, backend evaluation services, and structured report generation to the investor dashboard.
Data Sources
Property data feeds, market indicators, and geospatial APIs provide the raw inputs for the evaluation pipeline.
Market APIs · Property Data Feeds · Geospatial APIs
ETL / Data Processing Layer
Apache Airflow DAG orchestration drives Python-based extraction, transformation, validation, and dual-database loading of aggregated data.
NestJS Backend Services
Modular NestJS services expose RESTful endpoints, enforce business logic, and integrate with external data providers through clean adapter layers.
Analysis Engine
Structured evaluation logic processes aggregated property and market data to produce consistent, comparable investment metrics and scoring.
Report Generation Layer
Processed evaluation outputs are structured into analytical reports with actionable investment recommendations and geospatial context.
Structured Reports · Investment Insights · Google Maps API
Next.js Frontend Dashboard
Reporting dashboard built with Next.js and Atomic Design — presenting investment insights, property comparisons, and geospatial data to investors.
Next.js · Atomic Design · Serverless · Data Visualisation
Engineering
Technical highlights
Backend Data Aggregation Services
Built NestJS services to aggregate property-level metrics and market indicators from multiple external data sources through clean adapter layers — keeping integration logic isolated from evaluation business logic.
Structured Evaluation Logic
Implemented structured property evaluation logic that processes aggregated data and produces consistent, comparable investment metrics — transforming raw inputs into decision-support outputs.
ETL Pipeline Contributions
Worked on Apache Airflow DAG-based pipelines for property and market data ingestion. Python extraction and transformation tasks load processed records into MongoDB and PostgreSQL via Prisma.
Google Maps API Integration
Integrated Google Maps Places API (Autocomplete) and geospatial context into property records — enriching evaluation data with location intelligence for investment analysis.
Atomic Design Frontend Architecture
Built and structured the Next.js reporting dashboard using Atomic Design principles — atoms, molecules, organisms, templates — ensuring consistent, reusable UI components across all reporting views.
Serverless Component Adoption
Introduced serverless components for stateless compute tasks where they improved efficiency and deployment flexibility without adding operational overhead to the main backend.
Stack
Technologies used
NestJS · TypeScript · Next.js · Python · Apache Airflow · MongoDB · PostgreSQL · Prisma · Google Maps API · Serverless · Atomic Design · Docker
More work
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Main backend developer on a production multi-tenant B2B gifting platform. Engineered a modular, extensible NestJS codebase covering multi-tenant isolation, product variation modeling, async SQS worker patterns, and integrations with Stripe, Shopify, Xero, and AWS S3.
NestJS · TypeScript · AWS SQS · Stripe
Marketplace Backend Modernization
Modernized a legacy NestJS marketplace backend using Hexagonal Architecture and quickly ramped up on an existing Apache Airflow DAG-based ETL system to implement data processing enhancements and workflow improvements.
NestJS · Airflow · PostgreSQL · MongoDB
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