DUSHYANTH KUNJAM

Designed and delivered scalable cloud data engineering solutions using AWS and Microsoft Azure for large-scale data processing, analytics, and enterprise data integration. Developed batch and streaming data pipelines using Apache Spark, PySpark, Kafka, Apache Flink, Python, and cloud-based data services to ingest, transform, validate, and process high-volume datasets Developed and maintained Azure Data Factory pipelines for data ingestion, transformation, orchestration, and movement between enterprise data sources and cloud data platforms Developed AWS Glue ETL jobs and workflows to extract, transform, validate, and load large-scale datasets into Amazon S3 and Amazon Redshift. Built cloud-based ETL/ELT workflows across AWS and Azure to support batch processing, data transformation, validation, and downstream analytics. Worked with Hadoop, HDFS, Hive, Spark, and PySpark to process distributed datasets and support large-scale analytics and machine learning workloads Implemented event-driven data processing using Apache Kafka and Apache Flink for real-time ingestion, transformation, and streaming analytics Developed and optimized complex SQL queries, joins, aggregations, stored procedures, and data transformations using PostgreSQL for enterprise data processing and analytical workloads. Integrated Apache Iceberg with Apache Spark/PySpark for scalable data processing and optimized lakehouse table operations Built AWS data lake pipelines using Amazon S3, AWS Glue, and Amazon EMR for scalable ingestion, transformation, and processing of large datasets Integrated Airflow workflows with AWS services including S3, EMR, Glue, Lambda, and CloudWatch to automate data ingestion and processing workflows Developed real-time streaming pipelines using Apache Kafka and Amazon Kinesis for high-volume event ingestion and downstream data processing. Optimized Redshift query performance through data distribution, sort keys, compression, and query optimization techniques Developed and maintained Apache Airflow DAGs to orchestrate end-to-end data pipelines across Amazon S3, AWS Glue, EMR, Lambda, and Redshift, including scheduling, dependency management, monitoring, and failure handling. Performed enterprise-scale data migration activities across on-premises and cloud environments, developing data extraction, transformation, validation, and loading pipelines using Python, SQL, AWS Glue, Amazon S3, and Amazon EMR. Designed and implemented enterprise-scale cloud data migration pipelines using Python, SQL, AWS S3, AWS Glue, EMR, and Redshift to extract, transform, validate, and migrate large volumes of structured and semi-structured data. Developed Java-based data processing components and REST APIs to integrate enterprise applications with AWS/Azure-based data platforms and downstream services Built data integration workflows across SQL Server, Azure SQL, MongoDB, MongoDB Atlas, and cloud-based data platforms, supporting reliable data movement and analytics Leveraged Google Cloud Platform (GCP), BigQuery, Dataflow, Dataproc, and Cloud Storage for scalable data processing, analytics, and enterprise data engineering workloads Integrated Amazon Redshift with Amazon S3 and AWS Glue to support scalable data ingestion, transformation, and analytics workflows Developed and optimized Spark workloads on Amazon EMR for distributed batch processing and large-scale ETL pipelines Implemented data exchange and serialization workflows using JSON, XML, CSV, and Avro, including serialization and deserialization for batch and streaming pipelines Implemented secure enterprise integrations using JWT authentication, Kerberos, API security, and access controls across cloud and distributed data-processing environments Contributed to Data Mesh initiatives by developing reusable and governed data products across enterprise platforms, databases, and cloud environments Prepared and curated datasets for Data Science and ML teams, including data profiling, cleansing, transformation, aggregation, validation, and feature preparation. Developed automated testing for data pipelines and REST APIs and supported System Testing, UAT, integration testing, and end-to-end testing within CI/CD environments Used Linux/UNIX environments and Bash shell scripting to automate data-processing tasks, execute batch jobs, manage files, and support cloud data pipeline operations. Developed and optimized Amazon Redshift data warehouse solutions for large-scale analytical workloads using complex SQL queries, joins, aggregations, and transformations Collaborated with architects, data engineers, data scientists, and business stakeholders to resolve complex technical challenges and deliver scalable AWS/Azure data solutions Built ETL/ELT pipelines to load and transform structured and semi-structured data into Amazon Redshift from AWS data sources

United Airlines

Worked on AI and machine learning solutions for financial services, focusing on intelligent document processing, NLP, predictive analytics, and business automation. Developed ML and deep learning models using structured and unstructured financial data and, from 2023 onward, worked with Generative AI, LLMs, embeddings, and RAG for document understanding and enterprise knowledge retrieval. Developed AI/ML solutions using Python, Scikit-learn, TensorFlow, and PyTorch for financial analytics, document processing, and business automation Developed AI-agent workflows using LangChain and LangGraph to support enterprise AI use cases and integrate LLM applications with data and application services Worked with cloud Data Lake/Data Warehouse architectures to organize and serve structured and semi-structured data Built and optimized classification, regression, clustering, NLP, and deep learning models for business use cases Developed data transformation and validation pipelines using Databricks/PySpark and SQL to prepare analytical datasets for downstream ML and business applications Developed scalable Databricks and PySpark data pipelines to ingest, transform, cleanse, and validate large-scale structured and semi-structured datasets for downstream analytics and machine learning applications. Developed, trained, deployed, and monitored machine learning models using Amazon SageMaker, integrating SageMaker workflows with AWS data pipelines and enterprise applications Developed data integration workflows between enterprise data sources and Snowflake to support scalable analytical processing, data transformation, and reporting workloads. Developed complex PostgreSQL and SQL queries involving multi-table joins, aggregations, window functions, CTEs, and data transformations to prepare enterprise datasets for analytics and ML workloads. Integrated Snowflake with enterprise data workflows to support scalable analytical processing and reporting Evaluated ML models using Precision, Recall, F1-score, ROC-AUC, RMSE, and MAE based on business requirements Integrated ML models with enterprise data pipelines and applications through REST APIs and cloud-based services Developed Generative AI and LLM-based solutions for document understanding, summarization, information extraction, and knowledge retrieval Designed and optimized enterprise Generative AI workflows for document understanding, summarization, information extraction, and knowledge retrieval using LLMs, prompt engineering, embeddings, and RAG Built RAG pipelines using document ingestion, chunking, embeddings, metadata, semantic search, and contextual response generation Worked with OpenAI and Azure OpenAI APIs, prompt engineering, embeddings, and vector search for enterprise GenAI applications Improved LLM response quality through prompt optimization, context management, grounding, and response validation Supported model monitoring, performance analysis, troubleshooting, and optimization across development and production environments Used Git and CI/CD practices to manage and deploy AI/ML application components Collaborated with data engineers, business stakeholders, and subject-matter experts to identify AI use cases and deliver scalable technical solutions

BNY

Worked on data science and machine learning initiatives focused on customer analytics, personalization, demand forecasting, and business optimization. Analyzed customer, product, transaction, and behavioral data to develop predictive models and deliver insights that supported customer experience and business decisions. Analyzed large-scale customer, transaction, product, and behavioral data using Python and SQL to identify trends and business opportunities Performed data preprocessing, exploratory data analysis, feature engineering, and statistical analysis for machine learning projects Developed classification, regression, clustering, and ensemble models using Scikit-learn for customer and business analytics Built predictive models for customer segmentation, demand forecasting, customer behavior, and personalization Developed recommendation models using customer interactions, product information, and purchase history to support personalized experiences Applied NLP techniques including text preprocessing, TF-IDF, sentiment analysis, and text classification to analyze customer feedback Developed selected deep learning models using TensorFlow and PyTorch for predictive analytics and NLP use cases Developed and maintained Azure Data Factory pipelines for data ingestion, transformation, orchestration, and movement between enterprise data sources and cloud storage Supported self-service analytics and dashboarding by developing Power BI reports and curated datasets that enabled business users to monitor KPIs and analytical insights Created Power BI dashboards and data visualizations using Matplotlib and Seaborn to communicate analytical findings and model results Optimized SQL queries and data extraction processes to efficiently process large datasets from relational data sources Developed reusable Python modules and notebooks for data preparation, model training, validation, and reporting Supported model deployment through REST APIs and automated data workflows and monitored model performance and data quality Collaborated with data engineers, business analysts, and stakeholders to translate business requirements into scalable data science solutions

Macy's

Worked on data science and machine learning solutions to analyze business and operational data, identify trends, build predictive models, and support data-driven decisions. Developed end-to-end workflows covering data preparation, exploratory analysis, feature engineering, model development, validation, and visualization. Collected, cleaned, and analyzed structured and semi-structured data using Python, Pandas, NumPy, and SQL Performed exploratory data analysis (EDA) and statistical analysis to identify trends, patterns, correlations, and data-quality issues Developed classification, regression, and clustering models using Scikit-learn for business and operational use cases Performed feature engineering, feature selection, data normalization, and model tuning to improve model performance Applied statistical techniques to identify relevant variables and improve the reliability of analytical results Evaluated models using cross-validation, Precision, Recall, F1-score, ROC-AUC, MAE, and RMSE Developed reusable Python scripts and Jupyter notebooks for data preparation, model training, testing, and analysis Used SQL for data extraction, joins, aggregations, transformations, and analytical queries across relational databases Created dashboards and visualizations using Power BI, Matplotlib, and Seaborn to present insights to business stakeholders Collaborated with data engineers to prepare reliable datasets and support machine learning data pipelines Documented data analysis, modeling approaches, assumptions, and results for technical and business teams Supported model validation, performance monitoring, and deployment of analytical solutions into business workflows

Enmovil