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Project Duration
6 months
Client Industry
Real Estate
Target Markets
USA

Marcus and Millichap US

Centralized Data Infrastructure for Regional Email Performance Insights and Scalable Marketing Intelligence

Technology Stack

No items found.

Client Overview

Challenges in Modeling Subscription and Business Metrics

Marcus & Millichap is a leading real estate investment services firm specializing in commercial property sales, financing, research, and advisory across the United States. The company offers data-driven solutions to investors and institutions by combining market intelligence with a broad network of brokers. With a focus on the real estate finance sector, Marcus & Millichap supports clients in making informed decisions through comprehensive property and financial data integration.

Solutions Delivered

Unified warehouse combining Salesforce, Sheets, API data Automated ETL pipelines for scalable data processing Role-based security for financial data access

Dynamic dashboards tailored to operations and finance

Team Composition

Data Engineer

Python Developer / ETL Specialist

BI Developer / Looker Studio Analyst

Data Governance & Compliance Specialist

Engagement Type

Hourly Contract

Key Challenges

Challenges in Scaling and Standardizing Financial Data Operations

Data Complexity

Needed to unify structured and unstructured financial data from various systems


Scalability & Performance

Struggled to handle increasing data volume from APIs and manual inputs

Data Consistency

Lack of automation led to inconsistent financial reporting and insights


Strategic Roadmap

To meet growing operational demands, we needed a scalable data pipeline that could unify financial and property data from diverse sources like Salesforce, Google Sheets, and external APIs. The goal was to automate ingestion, enforce consistent transformation logic, and maintain high data quality. Equally important was enabling real-time access to key insights through dashboards, ensuring finance and operations teams could make fast, data-driven decisions without relying on manual reporting.

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To support timely financial analysis and operational visibility, we built a fully automated data pipeline that unified information from Salesforce, Google Sheets, and third-party APIs. Using Python, we scripted data extractions and integrated them into scalable ETL pipelines for cleansing, normalization, and transformation.

Execution Approach

Automated, Scalable Architecture for Unified Financial Data Analytics

To support timely financial analysis and operational visibility, we built a fully automated data pipeline that unified information from Salesforce, Google Sheets, and third-party APIs. Using Python, we scripted data extractions and integrated them into scalable ETL pipelines for cleansing, normalization, and transformation.
All data was centralized in BigQuery to establish a reliable source of truth.
On top of this infrastructure, we developed real-time dashboards in Looker Studio, empowering finance and operations teams with interactive, role-specific insights. The system reduced manual reporting effort, increased data accuracy, and enabled faster, data-backed decision-making across business functions.

BUSINESS IMPACT

Streamlined Reporting Workflows
Accelerated Financial Decision-Making
Improved Data Governance Compliance
Increased Operational Efficiency

50+

hours/week saved from manual ETL, increasing team productivity.

30%

reduction in operational costs via automation and streamlined workflows.

70%

fewer data processing errors, enhancing reporting accuracy and trust.