Samita Ambade

Technologies: AWS Glue, Amazon S3, AWS Lambda, Amazon Redshift, PySpark, Python, SQL, Databricks

• Designed and developed end-to-end ETL pipelines to integrate data from multiple sources, such as sharepoint, bulk upload API ,Salesforce and internal systems.
• Implemented a data ingestion framework to load structured and semi-structured data into Amazon S3 (data lake).
• Performed data cleansing, transformation, normalization, and validation to ensure high data quality and consistency
• Built scalable data processing workflows using PySpark and Databricks for handling large datasets
• Designed and implemented data models using fact and dimension tables (Star Schema) for analytical reporting
• Optimized Spark jobs using partitioning, caching, and predicate pushdown to improve performance
• Automated workflows and event-driven processing using AWS Lambda
• Loaded transformed data into Amazon Redshift for reporting and business intelligence use cases
• Ensured data security and access control using IAM roles and policies

Impact:
Improved data quality, reduced processing and reporting time, and enabled a unified and scalable data platform for better business decision-making

Technologies: Azure Data Factory, Azure Blob Storage, Python, Couchbase, Power BI, Azure DevOps

• Developed automated data ingestion pipelines to extract data from emails and multiple file formats using Azure Logic Apps
• Stored raw data in Azure Blob Storage (Landing Zone) and designed structured data flow architecture
• Implemented data transformation and validation logic using Python with JSON and YAML configurations
• Processed large-scale logistics data and stored it in Couchbase (NoSQL) for high scalability and performance
• Built machine learning models to predict delivery timelines and classify cargo based on demand and capacity
• Automated pipeline execution, monitoring, and notifications using Azure DevOps CI/CD pipelines
• Created dashboards and reports using Power BI for business insights

Impact:
Reduced manual effort, improved delivery prediction accuracy, and enhanced overall logistics efficiency and customer satisfaction

Technologies: Python, MySQL, Django, XGBoost, AWS CloudWatch, Git

• Extracted and processed large volumes of data from MySQL databases for analysis
• Performed data cleaning, transformation, and feature engineering using Python (Pandas)
• Built and trained machine learning models (XGBoost, regression) to predict project costs and budget utilization
• Developed REST APIs using Django to deploy and integrate models into applications
• Monitored system performance and logs using AWS CloudWatch
• Collaborated with stakeholders to improve cost estimation and resource allocation strategies

Impact:
Achieved 94% prediction accuracy, improved budgeting efficiency, and reduced cost overruns