Client Sagard Holdings

Role Principal Software Engineer

Timeline 12 months

Python • React • Google Cloud Platform • BigQuery • DBT • PostgreSQL • REST APIs • Data Engineering • Analytics

Sagard Holdings wanted to modernize the way its investment teams sourced and evaluated private companies.

Instead of relying on spreadsheets, market reports, and manual research, the goal was to build an internal platform capable of continuously aggregating company data, analyzing investment signals, and surfacing promising startups based on customizable criteria.

As part of the engineering team, I helped build the backend services, analytics pipelines, and cloud infrastructure that powered this investment intelligence platform, enabling analysts to discover opportunities faster through data-driven insights. The platform combined a Python backend with React, BigQuery, and DBT running on Google Cloud Platform to deliver a responsive, analytics-first experience for investment professionals. This work included building scalable cloud services, REST APIs, and data pipelines to support investment research and recommendations.  

The platform was designed to solve several key business challenges:

Reduce the time required to identify potential investment opportunities

Consolidate fragmented startup and funding datasets into a single platform

Enable analysts to search, compare, and evaluate companies using rich datasets

Build scalable cloud-native infrastructure capable of processing millions of records

Create reusable data models that could evolve alongside changing investment strategies

Deliver actionable insights instead of raw data

Designed and implemented backend services in Python responsible for powering the investment platform.

Responsibilities included:

REST API development

Business logic implementation

Authentication

Data aggregation

Search APIs

Analytics endpoints

Developed large-scale analytics pipelines using BigQuery and DBT to transform raw company data into investment-ready datasets.

This included:

ETL pipelines

Data normalization

Derived analytics models

Reporting datasets

Scheduled transformations

Built scalable cloud-native services on Google Cloud Platform.

Key components included:

BigQuery

Cloud Storage

Managed compute services

Cloud-native deployment pipelines

Collaborated closely with frontend engineers building the React application.

Designed APIs that supported:

Company search

Investment dashboards

Filtering

Company profiles

Recommendations

Analytics visualizations

Investment data originated from numerous providers, each with different schemas, formats, and quality standards.

One of the biggest engineering challenges was creating reliable transformation pipelines capable of normalizing this information into a consistent internal model while maintaining high performance as datasets continued growing.

Investment analysts expected search and analytical queries to return almost instantly despite querying millions of records.

This required careful optimization of:

BigQuery datasets

Data models

API responses

Query execution

Data caching

Analysts frequently requested new filters, scoring models, and investment signals.

Rather than tightly coupling the frontend to specific reports, we designed APIs that exposed reusable datasets and supported flexible querying without requiring constant backend changes.

The platform consisted of several independent layers working together.

External providers continuously supplied:

Startup information

Funding history

Executive information

Industry classifications

Geographic data

Market intelligence

Company metadata

Incoming data was transformed using DBT and BigQuery into analytical models optimized for investment research.

These datasets powered dashboards, company comparisons, and recommendation logic.

Python backend services exposed REST APIs consumed by the React frontend.

The application handled:

Search

Authentication

Company profiles

Analytics

Recommendations

Saved searches

User preferences

Investment teams could:

Search companies

Compare startups

Discover emerging industries

Explore funding rounds

Identify growth signals

Track investment opportunities

Python backend services

React frontend integration

Google Cloud Platform deployment

BigQuery analytical warehouse

DBT transformation pipelines

REST API architecture

Data engineering workflows

Cloud-native architecture

Large-scale analytical datasets

Throughout the project I was responsible for:

Designing backend architecture

Building Python services

Developing REST APIs

Creating cloud-native data pipelines

Optimizing BigQuery performance

Building DBT models

Supporting frontend integration

Improving application performance

Participating in architectural decisions

The finished platform transformed how investment teams sourced new opportunities.

Instead of manually reviewing multiple disconnected datasets, analysts could search and evaluate companies from a centralized platform that surfaced meaningful investment signals and growth indicators.

The platform delivered:

✔ Faster investment research

✔ Centralized company intelligence

✔ Scalable analytics infrastructure

✔ Improved data quality through standardized pipelines

✔ Cloud-native architecture capable of supporting future growth

Python

SQL

JavaScript

React

Google Cloud Platform

BigQuery

DBT

REST APIs

Data Engineering

Analytics

Cloud-native Services