Trinadhreddy Seelam

About Me

I am Trinadhreddy Seelam, a dedicated graduate completing a Master's degree in Computer Science at the University of Massachusetts Boston, graduating May 2025. My journey in the tech world has been driven by a passion for data engineering and a commitment to excellence in every project I undertake.

I worked as a Data Engineer at Futurense Technologies, where I had the privilege of collaborating with OPTUM on data warehousing projects. I played a crucial role in the seamless migration of data from Teradata to Snowflake and the transition from DataStage to PySpark, all within an Agile framework. My responsibilities included developing and maintaining data integration processes, implementing data models, ETL processes, and data pipelines for the Data Warehouse.

In addition to my work at OPTUM, I recently completed a Data Analytics internship with EMR Technical Solutions. There, I spearheaded the Indexer Project, developing an ETL process using Python and Shell scripting to load essential blockchain data into databases.

Currently, I am a Graduate Teaching Assistant at the University of Massachusetts Boston, where I provide support through curriculum development, student consultations, and the assessment of student work. This role has further solidified my passion for both learning and teaching.

My technical expertise spans a wide range of tools and technologies, including Python, SQL, Java, C, MySQL, PySpark, UNIX shell scripting, Airflow, Hive, Sqoop, Snowflake, Teradata, Kafka, Hadoop, DataStage, Informatica, Flask, Power BI, GitHub, AWS, and Azure. I am proficient in data loading, automation, and data comparison, utilizing tools such as SnowSQL, shell scripting, and the azcopy utility.

A proud graduate of Rajiv Gandhi University of Knowledge Technologies with a Bachelor's degree in Computer Science and Engineering, I maintained an exemplary academic record, boasting a CGPA of 8.9.

As I continue to grow professionally, I am actively seeking full-time opportunities where I can leverage my skills and contribute to organizational success. Let’s connect and explore how we can collaborate!

#FullTimeOpportunities #DataEngineering #TechProfessional #ComputerScience

Experience

Graduate Teaching Assistant – University of Massachusetts Boston

Jan 2024 – May 2025 | Boston, MA

• Provided academic support by assisting with curriculum development, student consultations, and evaluating coursework, incorporating Python, machine learning, and data analysis tools.
• Created an Autograder using Docker, Python, and unit testing frameworks to automate student assessment evaluations, significantly reducing manual efforts by 90% while improving evaluation efficiency and consistency.
• Guided students in implementing machine learning models, understanding large language models (LLMs), and optimizing Python scripts for academic projects.

Data Analytics Intern – Circe Bioscience

Oct 2024 – Dec 2024 | Boston, MA

• Automated manual Excel processes by developing a Python-based framework using Pandas, NumPy, and Unix shell scripting, optimizing execution flow, achieving 100% data accuracy.
• Delivered interactive Power BI dashboards to provide stakeholders with real-time, actionable insights.
• Enhanced workflow orchestration by implementing Apache Airflow DAGs to schedule and monitor data processing jobs, ensuring pipeline reliability and failure recovery.

Data Analytics Intern – EMR Technical Solutions

May 2024 – Aug 2024 | Boston, MA

• Developed and implemented an ETL process using Python and Shell scripting, efficiently extracting, transforming, and loading blockchain data into PostgreSQL databases, improving processing speed by 70% and achieving 99% accuracy.
• Implemented logging and error-handling mechanisms within the ETL pipeline, reducing debugging time by 50% and ensuring system reliability.

Data Engineer – Futurense Technologies Pvt Ltd (Client: Optum)

May 2022 – Aug 2023 | Bangalore, India

• Led successful migrations from Teradata to Snowflake and DataStage to PySpark using Agile methodologies, resulting in a 20% increase in data processing efficiency.
• Utilized Azure Data Factory for converting DataStage jobs into PySpark (Spark SQL), reducing job execution time by 25% in the BDPass environment.
• Developed and maintained data integration processes, data models, ETL processes, and pipelines for Healthcare Data Warehouse, contributing to a 30% improvement in data accuracy.
• Led the successful execution of quarterly Production and UAT runs, meeting 100% of customer SLAs and reducing system downtime by 15%.
• Automated data validation between Azure Cloud Blob storage and on-premises systems using Shell scripting and Azcopy utility, reducing manual testing effort by 90% and ensuring 99% accuracy.
• Facilitated production deployments by managing code migration to Git and executing CI/CD pipelines with Jenkins. Resolved 95% of issues within 24 hours, ensuring seamless operations and minimal downtime.
• Integrated Unix Shell Scripting, DataStage, Apache Airflow, Snowflake, Teradata, Databricks, and Azure Data Factory to streamline ETL workflows, significantly enhancing the reliability and efficiency of data processing operations.

Associate Data Engineer – Futurense Technologies Pvt Ltd

Nov 2021 – Apr 2022 | Bangalore, India

• Completed comprehensive training on Big Data and ETL tools including Hadoop, Hive, Sqoop, PySpark, Airflow, Kafka, and Informatica, enhancing ability to manage and process large datasets efficiently.
• Implemented Slowly Changing Dimensions and developed efficient data pipelines using Hive and Spark.
• Designed a robust Railway Management System schema using SQL to optimize database performance.

Education

• Master of Science in Computer Science — University of Massachusetts Boston (05/2025) | CGPA: 3.967/4
• B.Tech in Computer Science — Rajiv Gandhi University of Knowledge Technologies Ongole, India (05/2022) | CGPA: 8.9/10

Technical Skills

• Programming Languages: Python, SQL(MySQL, Oracle, Snow SQL, Spark SQL), Java, Scala, C, Unix Shell Scripting
• Big Data Tools: PySpark, Hadoop, Hive, Sqoop, Airflow, Kafka, Databricks
• Machine Learning & AI: PyTorch, TensorFlow, Scikit-learn, LLM, SLM, RAG, LangChain, Open AI
• ETL Tools: Datastage, Informatica
• Databases: Snowflake, Teradata
• Cloud: Amazon Web Services, Microsoft Azure
• Data Visualization: Power BI
• Other: Git, Flask, Rally, Pandas, Numpy

Certifications

Projects

• Stock Sentinel - AI Agent for Stock Market analysis - AI-powered stock market intelligence platform that leverages multiple specialized AI agents to deliver real-time market analysis, financial news sentiment tracking, and natural language based portfolio insights and alerts powered by advanced LLMs, NLP, and ML algorithms working in concert with streaming market data and vector databases.
• HeartChecker - Heart health monitoring web application - Developed and deployed HeartChecker, a Python Flask-based cardiovascular health monitoring platform, incorporating machine learning for early detection, AWS Elastic Beanstalk for deployment, and Plotly for interactive data visualization.
• Pneumonia Detection Using Chest X-Ray Images - Investigated pneumonia detection using the Kaggle Chest X-Ray Images (Pneumonia) dataset. Implemented transfer learning with ResNet50 and MobileNetV2, and developed a custom CNN. Leveraged data augmentation to enhance performance, achieving 84% accuracy, while identifying areas for improvement to meet clinical application standards.
• SCD Implementation using Hive - Implemented Slowly Changing Dimensions (SCD) using big data tools such as MySQL, Hadoop, Hive, and Sqoop ensuring efficient handling of changing data and maintaining data integrity.
• Movie Booking Site using Python Flask - Developed and deployed Python Flask-based Movie Booking Site, leveraging AWS services including RDS and Elastic Beanstalk.
• Music Player using Flutter - Music Player developed with Flutter to play local mp3 files in phone storage, compatible with both Android and iOS.
• AI Chatbot GUI using Python Tkinter - AI chatbot with GUI using Python Tkinter that uses NLP (Natural Language Processing) and takes Article as input, responding to user commands based on that Article.