Designed and implemented robust ETL pipelines, reducing data processing time by 30% and ensuringdata accuracy and integrity. Extracted data from Salesforce API, integrating it into ETL pipelines, which led to a 40% improvement indata retrieval speed and enabled real-time updates. Developed predictive models using various machine learning tools to forecast customer behavior, churn,and calculate Customer Value (CV) and Lifetime Value (LTV). Utilized Salesforce Commerce Cloud, Marketing Cloud, and Open Commerce APIs to allow external webapplications to leverage core e-commerce functionalities. Created and managed complex workflows using AWS Step Functions, automating data processing tasksand reducing manual intervention. Architected and deployed data pipelines using AWS CloudWatch, AWS Lambda, SNS, SQS, Glue DataCatalog, and Athena, leading to a 25% reduction in data query response times and efficient management ofdatasets exceeding 1TB. Leveraged SQL and DBT (Data Build Tool) to transform and model data, establishing a reliable andmaintainable data infrastructure that decreased data preparation time by 40%. Worked closely with cross-functional teams to understand data requirements, providing technicalguidance and support throughout the project lifecycle, contributing to a 15% increase in project deliveryspeed. Streamlined data processing workflows and optimized resource usage, resulting in a 20% reduction inoperational costs. Developed Python scripts for ETL processes, automating repetitive tasks and reducing processing time. Utilized Scala with Apache Spark to process and analyze large-scale datasets, achieving a 20% increase in processing efficiency and enhancing data pipeline performance.

Drive the interaction and partnership with various departments to ensure active cooperation in analyticalneeds and generate the pull-through of insights with the business. Responsible for generating customer-based insights and analytics that maximize ROI and help the businessmeasure marketing and promotions effectiveness. Analyzed data quality issues through exploratory data analysis using SQL, Python, and Pandas. Responsible for maintaining data quality monitoring and measurement, establishing and publishing KPIs andSLAs Ingested JSON data from different sources and processed the data to consume for various ML models usingNumPy, Pandas, and various Python libraries. Design, evaluate, and propose changes based on field or natural experiments to test the impact of theaddition, removal, placement, and space allocation of assortment in stores. Built data lineage sourcing data from different cloud platforms using data flow facilitate data scienceprojects and built business critical dashboards in Power BI and Tableau to monitor real-time sales Worked on various Google marketing platform tools performing cohort analysis, customer retention andengagement models, and support in delivering the best CX Led the migration from Teradata to BigQuery using GCP tools, resulting in an annual cost saving of $800kand an increase in performance by 20%. Design and implemented data pipelines to handle real-time streaming data to process semi-structureddata by integrating 10+ data sources using Python. Designed e-commerce data model maximizing load performance and building complex aggregated views forreporting resulting in shorter response time for analytical support. Experienced in creating ETL pipelines and building complex aggregated views to fulfill complex businessrequirements.

They were studied and revamped data dictionaries to include a more robust history for developing consistency across domains.Developed the steps for data cleaning, increasing the robustness of the data sets using R packages, resulting in 30% efficient Business Reporting over manual Excel reporting.Wrote complex aggregated views to answer business-specific questions and facilitate field reporting and dashboards.Led quarterly Financial Reports division formulating interactive decks in tableau for C-level executives.Experienced in working with cloud-based applications such as AWS and Google Cloud platform and provided help to business users in using various applications

Create and maintain reporting infrastructure to facilitate visual representation of manufacturing data for purposes of operations planning and execution.Performed predictive analytics to predicted customers’ risk for health insurance and modeled sales forecasting for retail industry.Performed feature engineering techniques to gain customers’ insights, reduce model complexity.Developed consumer behavior, key trends, finding ways to maximize department revenue and improve supply chain operations.Interpreted the data and identified the business trends and patterns in complex data sets and analyze results using statistical techniques using RUsed data manipulation tools such as reshape2, dplyr, tidyr in R for mining and implemented improved ideas to make the system more efficient.Performed diagnostic analytics to analyze complex business problems, used facts and data to determine the root cause of customer churn.Created Stored Procedures, Functions, Views, Triggers and wrote complex SQL queries to maintain the students AIS application efficiently.Tested ETL process before and after data validation process, messages published by ETL tool, data loaded into various databases and document data mapping from source to target.Generated AD-HOC reports and charts to statistically compare historical data and track findings.