Designed and implemented scalable data pipelines and ETL workflows to integrate laboratory data from the Sapio Lab Informatics Platform(cloud based API) into downstream SQL server systems for analytics and operational reporting. Built Python-based automation and event-driven services to enable real-time data ingestion, instrument integration, and workflow orchestration within the LIMS ecosystem.

Architected and maintained data models and backend services supporting high-volume laboratory data, ensuring reliability, data integrity, and system scalability. Collaborated cross-functionally with scientific and clinical teams to translate complex lab processes into efficient software solutions, while improving overall system performance through optimization of data processing and API integrations.

As one of two engineers at Ohalo Genetics, I built and scaled internal data platforms and full-stack applications to support high-throughput scientific research workflows. I designed and implemented scalable data pipelines and backend services, improving data processing efficiency by ~60% by eliminating key bottlenecks in the data lifecycle. My work involved processing high-volume datasets and supporting production data systems used across multiple teams, ensuring reliability, scalability, and consistent data availability.

I also designed and implemented a cloud-based data lake using Amazon S3, migrating and archiving structured and unstructured data from Benchling into S3 Glacier Deep Archive for long-term, cost-efficient storage. I developed ingestion workflows to handle diverse data formats, including JSON, XML, CSV, images, and genomic data (FASTQ), enabling centralized and durable data storage.

Additionally, I integrated REST APIs to automate laboratory workflows, reducing manual data entry by ~50%, and architected optimized database schemas, stored procedures, and SQL queries for efficient access to experimental data. I built data transformation pipelines and reporting layers to support analytics and visualization, while leveraging Docker to improve deployment speed and environment consistency. Throughout my role, I partnered closely with scientists and computational teams to translate complex research workflows into scalable data and software solutions.