• Lead the development of a generalized data ingestion pipeline supporting a variety of data sources to produce clean and valuable data to inform key stakeholders (Python, AWS, EC2, S3, Lambdas, Postgres, dbt).

• Designed and implemented full-stack internal applications (Python, React, Django), with focus on code quality, reliability, scalability and maintainability, to accelerate ML researchers, data scientists and business users velocity.

• Implemented and maintained large-scale infrastructure using terraform, Docker, AWS.

• Advocated and drove best practices throughout the organizations (CI/CD, Agile, security, testing, code reviews, documentation, etc.).

• Supported team through interviewing, mentorship of junior members, sprint planning and team lead responsibilities.

• Designed and implemented high-volume, high-availability data pipelines for medical data used in ML model training.

• End-to-end development of Python backend to support medical devices.

• Full-stack work across backend (Python) and frontend (Typescript, React) for internal and customer-facing tooling.

• Lead developer for Python UI-based applications for automation of manual, recurrent operations. Reduced errors by 90%+. Sped up processes from 10+ days to 1 day.

• Developed large-scale ETL pipelines from various data sources (excel, web, custom-formats) for automated reporting and insights generation with robust handling of customer-side schema changes.

• Built and maintained real-time dashboards, visualization, analysis and recommendations for customers.

• Developed web-scraping and ingestion tools to build a comprehensive dataset of books and success metrics of over 10,000 authors.

• Built reproducible data standardization and data munging procedures for cross-source linking, metadata consolidation and deduping.

• Handled testing and analysis of the dataset.

• Built and maintained ETL pipeline (Golang) for massive (100B+ points) amounts of client data, turning

raw data in various format into standardized DB records for futher use by data scientists.

• Proof-of-concept of cold recommendation system (i.e. for new users) based on previous activities and

feedback.