Data Engineer

• Data Pipeline Building: Assist in building asynchronous batch Python and SQL ETL pipelines to handle ingestion and cleaning of over 25 million business records for use in ML applications.
• Collaboration: Partner with co-interns to reduce runtime of various data pipelines by 75% through refactoring synchronous ETL Python scripts to asynchronous.
• Data Cleansing: Configure Docker containers on AWS EC2 instances for scheduled Python and SQL scripts that ingest and cleanse small business data for user search results, provide detailed documentation on configuration, use instructions, and troubleshooting.

• Data Entry: Enter license plates captured on the video system into the MTA database. Regularly hit the minimum daily quota of 2000 license plates with near 100% accuracy.

• Advisory: Provide exemplary consultation for clients to improve performance and efficiency for over 2000 of our clients’ production lines, saving 2 million+ tons of CO2, and $8.5 billion in user production lines.
• Data Architecture: Assist senior data architects to build digital twin designs on the Industrial Internet of Things (IIoT) platform for Fortune 500 companies like Nestlé, Kimberly-Clark, and Michelin.
• Data Pipelines/Data Warehousing: Create pipelines and standardize ETL processes and tools to automate data extraction for Machine Learning and manual reporting using Java and SQL to optimize the manufacturing process, creating custom calculations, and saving tons of CO2 waste and billions of dollars, while clients simply submit CSV files into our pipelines.

• Research Programming: Use SAS & SQL data manipulation techniques with ETL processes for Medicare claims in VIM to provide higher quality care while cutting 50%+ of costs.
• Technical Support: Prepare technical documentations of different projects like data tables, data dictionaries, readme documents, flow charts, and ad hoc files using Microsoft Applications to assist the TDI Center of Excellence (COE) team for enhance their medical research based on Medicare data, as well as provide statistical and programming support to the team for other major projects.

• Collaboration: Consult with project coordinators and lead analysts to find best practice analytics to improve healthcare delivery for roughly 35 million Medicare Fee-For-Service (FFS) beneficiaries and 150 thousand clinical cases from electronic health records and data warehouses like Northwell and Providence.
• Mentorship: Mentor new data analysts in their onboarding process, which includes, but is not limited to teaching new SAS/SQL queries and locate files in the HVHC directory.
• Payment Analytics: Support senior data analysts to create and analyze Medicare Value Based Payments by creating reports for Bundled Payments for Care Improvement – Advanced (BPCI-A) and Inpatient Quality Reporting (IQR) through Medicare claims using SAS and SQL data manipulation techniques.
• Quality Control: Implement proper Quality Control (QC) measures such as code reviews and Git backups to ensure the quality of analytical files and their deliverables are consistent and accurate, while using security best practices in a corporate environment to ensure safety of production data.