•Led B2B customer data migration from AT&T’s to Liberty’s Salesforce and using AWS Workforce, ensuring a seamless transition for terabytes of data.•Worked using Python using libraries Pandas, NumPy, PyPDF2, pdfplumber, to automate the extraction of customer data from AT&T PDF invoices stored on SharePoint.•Implemented data quality assessments post-migration using cleansing and normalization techniques, achieving a 98% accuracy score.•Employed SQL queries via Salesforce Inspector and Workbench to verify migrated data accuracy against original datasets.•Enhanced operational efficiency by 30% through automation of migration tasks using Python scripts, streamlining workflows.•Collaborated with cross-functional teams to define migration requirements using Jira, achieving 95% clarity in project documentation.•Configured AWS Workspace solutions for secure database access post-migration, ensuring compliance with data integrity standards and providing ongoing support.

•Designed a sales data pipeline using AWS Kinesis and AWS Lambda, processing real-time data from over 50 sources, resulting in a 60% reduction in latency.•Developed and orchestrated ETL workflows with Talend, automating the extraction, transformation, and loading of over 10 TB of sales data monthly, improving integration efficiency by 50%.•Directed real-time data streaming with Apache Kafka, ensuring 99.9% uptime and high throughput for constant sales data flow and processing.•Leveraged Apache Spark to process and aggregate 20 TB of historical sales data, reducing processing time by 40% and enhancing analysis capabilities.•Utilized Python with PySpark and Pandas for data processing, improving data quality and accuracy by 30% through distributed transformations and analysis.

•Leveraged AWS services, Python, and Tableau to optimize data analysis and enrich insights by using EC2 for scalable computing power to run complex queries and S3 for storing and processing large datasets.•Used RDS for structured data storage and retrieval and Lambda for serverless computing for data processing.•Enhanced data analysis efficiency by 25% making custom SQL queries using Python and Tableau for real-time data monitoring for faster insights and informed decision-making to optimize the data pipeline, resulting in a 40% increase.•Developed data validation rules and transformation logic using Pandas also enforced data enrichment methods, resulting in 15% gain in data context and metadata, using Apache Spark and Apache Hive use these techniques to extract valuable insights from the data and provide actionable recommendations to stakeholders.•Expanded data range by 20% by identifying new data sources and worked with data engineers to ingest data from various systems feeding a wider view of business operations to deliver better data analysis and use Tableau for data visualization.