Results-oriented Data Engineer with a Master's degree in Information Technology and deep expertise in Python and SQL. Skilled in building and maintaining ETL pipelines, automating data transformation processes, and collaborating with cross-functional analytics teams to resolve complex performance issues. Proficient in optimizing data extraction workflows and designing scalable database utilities. Committed to delivering reliable data solutions that drive business decisions and improve operational efficiency.
Results-oriented Data Engineer with a Master's degree in Information Technology and deep expertise in Python and SQL. Skilled in building and maintaining ETL pipelines, automating data transformation processes, and collaborating with cross-functional analytics teams to resolve complex performance issues. Proficient in optimizing data extraction workflows and designing scalable database utilities. Committed to delivering reliable data solutions that drive business decisions and improve operational efficiency.
- Developed and maintained complex ETL pipelines using Python and SQL to integrate clinical data streams, supporting 94% accuracy in data reporting.
- Collaborated with analytics teams to design data transformation frameworks, reducing manual processing time by 15%.
- Built and improved SQL-based reporting utilities to facilitate seamless data extraction from large relational databases.
- Contributed to the development of automated unit testing suites to ensure high code quality within data integration workflows.
- Optimized database performance for high-volume datasets, resolving latency issues and improving system reliability.
- Implemented new data validation checks to ensure compliance with data governance standards and internal quality metrics.
- Designed and implemented scalable ETL processes that processed data for 400M+ users, resulting in a 20% increase in conversion efficiency.
- Optimized data extraction and manipulation workflows using Python (Pandas/Numpy), improving query understanding speed by 19%.
- Collaborated with backend teams to integrate data pipelines into enterprise services, ensuring 99.9% system availability.
- Built automated data quality checks and reporting scripts to support data-backed feature rollouts.
- Contributed to the refinement of database schemas and stored procedures to support growing business requirements.
- Integrated continuous testing into the development lifecycle, improving overall release stability.
- Developed and refined automated data extraction routines for market reporting, processing 120K+ daily trades accurately.
- Collaborated with audit teams to build reliable reporting pipelines, ensuring 100% compliance with regulatory reporting standards.
- Built and integrated low-latency streaming pipelines using Apache Kafka to handle peak market event volumes efficiently.
- Optimized SQL query performance across relational databases, reducing report generation time by 25% for business stakeholders.
- Developed reusable Python utilities and functions to standardize data flow patterns across departmental systems.
- Contributed to the maintenance of historical market databases, ensuring data integrity and accessibility for analytics.