Jp R. Email & Phone Number

United States

United States

United States

- Directed data cleansing projects using Databricks and Spark, processing over 1TB of banking data, resulting in a 95% improvement in data accuracy and enhancing its reliability for decision-making.- Handled large-scale banking data (over 10TB) by utilizing various Azure services, optimizing cloud data management, and reducing storage costs.- Enhanced query performance by implementing Snowflake’s clustering and caching features, speeding up data retrieval and streamlining analysis.- Built and maintained data pipelines in Snowflake using SnowSQL and SnowPipe, ensuring continuous data flow and transformation.- Automated the processing of banking data using JSON scripts in Azure Data Factory, cutting manual work by 75% and ensuring timely data delivery.- Improved ETL processes with Spark and Azure Data Factory, reducing processing time by 60% and enhancing data quality.- Designed Informatica Mappings and Sessions to efficiently load data from various sources, including flat files and Oracle databases.- Enhanced data security and ensured compliance by using Informatica’s metadata management and lineage tracking features.- Created Power BI dashboards and reports that provided real-time healthcare insights, improving resource allocation by 25%.- Developed SQL scripts with Airflow Operators to automate complex workflows, increasing operational efficiency by 40%.- Leveraged Databricks for interactive banking data analysis, generating insights that boosted data-driven initiatives by 15%.- Developed custom adapters to speed up banking data transfers to Azure Data Lake, cutting ingestion time by 70%.- Enhanced SQL scripts with PySpark to address data challenges, improving system performance by 25%.- Implemented CI/CD practices for banking apps on Azure, reducing deployment time by 50% and achieving 99% system uptime.

United States

- Designed and implemented a strategy to create and deploy 10 automated, scalable data pipelines that processed millions of records using Amazon Redshift, Data Grip, Amazon S3, and Glue, resulting in streamlined and efficient data operations.- Enhanced the performance of AWS databases, particularly Amazon RDS and Redshift, by optimizing queries and applying indexing techniques, leading to a 32% increase in processing efficiency for data storage and retrieval.- Utilized AWS Athena to run Scala-based queries on CSV files stored in AWS S3, streamlining data extraction and transformation processes to improve data accessibility and availability.- Developed Python scripts to automate the extraction of data from AWS S3 and load it into SQL Server, providing key data access for business teams.- Worked efficiently within the Databricks Delta Lake environment on AWS, using Spark to process and analyze complex datasets at scale.- Performed large-scale ETL operations on extensive datasets using Python, Spark SQL, S3, and Redshift, providing critical insights that supported business decision-making.- Leveraged Spark's in-memory processing capabilities to manage large datasets in the S3 Data Lake, applying filters and loading the results into Hive external tables to ensure efficient data access.- Automated the CI/CD pipeline using AWS CodePipeline, Jenkins, and AWS CodeDeploy, creating a seamless process for continuous software deployment.- Developed PySpark scripts to manage ETL workflows, integrating S3 data crawlers and establishing a data catalog for improved metadata management and data accessibility.

- Developed comprehensive business requirements based on user stories, utilizing agile methodologies to ensure continuous improvement and alignment with business goals.- Created Talend workflows within a Big Data environment, optimizing Spark’s in-memory processing to handle large datasets stored in the S3 Data Lake.- Demonstrated expertise in designing and improving SQL structures, including stored procedures, functions, views, indexes, and triggers, to enhance data access and processing efficiency.- Led large-scale ETL operations using Python, SparkSQL, S3, and Redshift, analyzing multi-terabyte datasets to uncover valuable customer insights and business intelligence.- Conducted thorough architectural assessments and implementation evaluations of AWS services, particularly Amazon EMR, Redshift, and S3, ensuring the creation of a strong data infrastructure.- Utilized AWS EMR to manage data transformations and efficiently transfer large data volumes between AWS repositories, including S3 and DynamoDB.- Applied Python across various tasks, including data processing, transformation, and automation, to improve ETL workflows and maintain data consistency.- Possessed deep expertise in AWS core services, including S3, EC2, IAM, and RDS, with hands-on experience in orchestrating data pipelines using AWS Step Functions, Data Pipeline, and Glue.- Leveraged AWS Glue dynamic frames and PySpark for complex data transformations, systematically cataloging processed data to improve accessibility.- Integrated Power BI with AWS data sources like Redshift and S3, enabling robust data analysis and visualization for business reporting.- Developed and managed IAM policies for secure and delegated administration in AWS, configuring users and roles to enforce role-based access control.- Contributed to the creation of a robust CI/CD pipeline using Jenkins, Terraform, and AWS, automating data transformation and ensuring seamless data flow across AWS environments.

- Designed and optimized SQL queries, scripts, stored procedures, and functions to improve data extraction and processing for real-time analysis. Implemented data validation and cleansing in Tableau and Excel, ensuring consistent data quality across business platforms.- Developed and maintained Tableau dashboards to track KPIs for sales, finance, and inventory, supporting data-driven decisions and strategic planning.- Used Python libraries such as NumPy and Pandas to analyze experimental data, A/B tests, and hypothesis-driven studies, contributing to a 10% increase in decision-making accuracy.- Created and delivered predictive analytics reports using Python and Data Studio, visualizing model results and performance with tools like Pandas, NumPy, and Matplotlib.- Applied statistical techniques using R and NumPy to analyze large datasets, revealing valuable trends and patterns that improved analysis accuracy by 15%.- Developed advanced SQL queries to identify key business trends, which led to a 15% reduction in hiring times and a 10% improvement in employee retention.- Monitored and optimized AWS resources using CloudWatch and Cost Explorer, ensuring efficient and cost-effective cloud usage for data engineering operations.- Used AWS S3 for secure, scalable data storage and AWS Lambda for serverless data processing, enabling seamless data transformations between systems.- Possess expertise in Data Analysis, Data Warehousing, Data Migration, Data Cleansing, Statistical Modeling, and SQL programming, ensuring a well-rounded approach to data management and analysis.