Healthcare

Data Engineering & Pipeline Optimization for a Healthcare Technology Company

A venture-backed healthcare technology company focused on cloud-based solutions for drug discount program management and pharmaceutical compliance. Backed by top-tier venture investors.

Duration
2024–ongoing
Team
3 data engineers
1 fullstack engineer
1 data analyst
Services
Data Engineering
Pipeline Optimization
Testing
Tech Stack
Python
PostgreSQL
SQL Server
Snowflake
dbt
AWS (RDS, Glue, S3, Fargate, CodePipeline)
Terraform
Docker
Tableau

The Challenge

The Challenge

As the platform scaled, data engineering capabilities became a bottleneck. Growing data volumes required faster and more accurate processing. Data models needed to be redesigned for scalability. Regulatory compliance in a sensitive healthcare environment demanded robust safeguards. Manual intervention in pipelines was creating errors and slowing delivery.

The Solution

We embedded a team of data engineers, a fullstack engineer, and a data analyst into the company's workflows, tackling performance issues while laying the foundation for future growth.

speed

Pipeline optimization

rebuilt the ingestion pipeline, increasing match rates by over 20%. Reduced memory usage by 3x, lowering infrastructure costs. Refactored using SOLID principles for improved code quality and maintainability.
verified

Data quality and safeguards

introduced incremental load logic with safeguards against corrupted data. Implemented dynamic validation schema to enhance reliability and minimize manual fixes.
architecture

Future-proofing

adapted applications and data warehouses to incorporate new data types. Enhanced CI/CD processes using advanced dbt features, cutting pipeline resource load by 15%.
bug_report

Comprehensive testing

developed a full testing framework (unit, smoke, visual regression, end-to-end), reducing failure rates and accelerating error detection.

Results

adjust

20% higher match rates

more accurate claim matching, strengthening compliance confidence.
memory

3x reduction in memory usage

significant cost savings and improved scalability.
trending_up

15% pipeline performance improvement

faster data availability for analytics and reporting.
architecture

Future-proof design

applications and warehouses handle new data types seamlessly.
rocket_launch

Faster innovation cycles

robust testing and CI/CD pipelines enable quicker, safer releases.
Why This Matters

In pharmaceutical compliance, the cost of getting data wrong isn't a dashboard error — it's a regulatory risk. This engagement delivered hard, quantifiable improvements: 20% higher match rates, 3x memory reduction, 15% faster pipelines. But the real value is engineering that understands what's at stake. Every optimization was designed with compliance guardrails built in, not bolted on after the fact.

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