Daniel P Email & Phone Number

United States

Virginia, United States

- Developed Python and SQL scripts for large-scale data cleansing, aggregation, and enrichment, reducing manual intervention by 50%. - Optimized ETL workflows with Apache Airflow, automating pipelines and reducing processing time by 40%. - Streamlined data storage using AWS S3 and Redshift, improving retrieval speeds and accessibility by 30%. - Designed cloud migration strategies to AWS (S3, RDS, Redshift), enhancing scalability and data availability. - Improved analytics with AWS Redshift and Snowflake, boosting query performance and reducing analysis time by 35%. - Structured and cleansed unstructured data like CSV and JSON, improving quality and reporting accuracy by 20%. - Applied data validation protocols, decreasing errors by 15% and ensuring high-quality data. - Utilized SQL Server and PostgreSQL for data modeling and complex queries, reducing runtime by 30%. - Built real-time data pipelines with AWS Lambda, reducing latency by 25% for faster reporting. - Extracted and synchronized data from 4+ sources, including APIs and user forms, ensuring timely updates and improved data integrity.

Hyderabad, Telangana, India

- Developed real-time data integration with dynamic API calls and Microsoft Graph API, improving data accuracy and collaboration for 100+ team members. - Automated workflows using RESTful APIs and Azure SQL Database, reducing manual data entry by 40% and boosting data processing efficiency by 30%. - Enhanced pipeline efficiency with Apache Kafka, reducing latency by 25% for real-time performance tracking. - Built interactive dashboards with Dash and Plotly, enabling self-service monitoring for 50+ team members and reducing manual input by 20%. - Strengthened data integrity by implementing data governance protocols with Apache Airflow, reducing ETL errors by 15% and improving reliability. - Secured API access with OAuth 2.0 and Azure AD, enhancing compliance and reducing unauthorized access by 35%. - Processed and analyzed large-scale client data with PySpark and Spark SQL, increasing transformation efficiency by 40%. - Orchestrated unstructured data processing with Apache Airflow, enabling efficient automation and faster HR insights. - Utilized HDFS for large-scale data storage and retrieval, supporting high-throughput processing. - Applied advanced SQL skills in PostgreSQL and SQL Server, reducing query runtime by 30% for faster data access.