Piotr – Python, SQL, GCP

A B2B healthtech platform enabling clinicians to design and communicate patient treatment procedure journeys. Built across three MVP iterations with full GDPR and HL7 compliance, earning pilots at six healthcare organizations.

• Delivered 3 GDPR- and HL7-compliant MVP iterations from scratch, securing pilots at 6 healthcare companies.
• Designed an anonymous-by-design data architecture that fell outside certification scope, saving $100k in compliance costs and 12 months of blocked development time.
• Won the MCSC Hospital Leadership Innovation Award and ranked in the Top 10 of the 2024 Unicorn Hub Startup Competition.

An analytical dashboard aggregating claims data from multiple US market sources for medical device companies. The platform processes 100 TB+ per run across diverse insurance and claims pipelines, supporting compliance-heavy healthcare analytics.

• Scaled the platform's processing throughput from 1 TB to 100 TB per run by redesigning the ingestion and transformation architecture.
• Designed, built, and administered a Databricks Data Platform to replace an EMR-based setup, saving $50k per month and one-third of the team's operational time.
• Evaluated two competing vendors by prototyping on real production logic and measuring cost, speed, and migration effort before committing.
• Established a testing framework and QA procedures that raised test coverage to 90% and halved the error rate.
• Authored the PySpark Practical Playbook, enabling data scientists to independently extend the Golden Layer to 6 new business domains.

A unified, self-service data platform serving multiple business departments, consolidating data from three legacy systems onto Snowflake. The platform streamlined reporting and analytics workflows across the organization.

• Built ETL flows from three legacy systems into the Unified Data Platform on Snowflake using dbt and Airflow.
• Refactored the shared data service library, reducing common scaffolding code from 100+ lines to 15.
• Simplified the stack by retiring Kafka and Databricks where unnecessary, cutting 20% of the codebase.
• Reduced data CI/CD pipeline build times from 20 minutes to 3.

A data platform identifying Key Opinion Leaders (KOLs) in European healthcare to accelerate Phase III clinical trials for pharmaceutical companies. The system ingested and processed web-scale data to surface influential clinicians for trial recruitment and engagement.

• Built data processing frameworks and reorganized pipelines, increasing throughput 30-fold.
• Developed the company's first cloud-based web crawling tool, enabling ingestion of 3 new external data sources.
• Grew the data team from 3 to 6 people and introduced delivery processes that halved the time to ship new data projects.

An internal optimization tool for sales teams at a global telecom equipment manufacturer, automating BTS configuration recommendations to maximize sales margins. Migrated from R to Python and scaled from a handful of users to hundreds.

• Migrated the BTS configuration optimizer from R to Python, added new features, and grew active users from a few to hundreds of sales representatives.
• Automated and stabilized the solution with monitoring and guardrails, delivering over $1M in annual cost savings.