Shashank Uppalamurthy

Designed, developed, and maintained SQL-driven ETL pipelines using Python and Snowflake to support analytics and ML workloads. Built scalable data ingestion pipelines integrating data from APIs, AWS S3, CRM platforms, and internal relational databases. Developed complex SQL transformations and designed Snowflake tables to support reporting and analytics. Implemented data validation and quality checks, maintained scheduled batch ETL processes, and produced analytics-ready datasets. Used AI-assisted tools (ChatGPT, GitHub Copilot) to accelerate development and explored LLM-based automation for data validation and internal documentation. Built NLP capabilities with Hugging Face transformers and developed interactive ML apps with Streamlit, integrating outputs with Snowflake for downstream analytics. Collaborated with data scientists and analysts in Agile teams and produced technical documentation.

Built data preprocessing and transformation pipelines using Python, Pandas, NumPy, and SQL to improve data quality for ML and analytics workflows. Processed large-scale datasets with Apache Spark on AWS EMR for batch and streaming analytics. Developed and optimized ML models using Scikit-learn, TensorFlow, and PyTorch for classification, regression, and forecasting. Designed predictive models (e.g., Random Forest, ARIMA) to drive data-driven insights. Leveraged Generative AI tools to accelerate model development and applied LLMB-based techniques for exploratory data analysis and rapid prototyping. Created SQL-based data extraction pipelines for ML training, troubleshot Spark performance, and delivered visualizations with Matplotlib/Seaborn. Documented data workflows and collaborated with cross-functional teams to align with business KPIs.

Designed and tuned ETL pipelines for large-scale data ingestion into Redshift. Used AI-assisted coding tools to improve SQL and Python scripting efficiency and automated routine data engineering tasks via prompt-based approaches. Developed and optimized SQL queries and stored procedures, supported analytics needs, and tuned Spark jobs on AWS EMR to improve batch processing performance. Implemented data governance controls (encryption, access permissions) using S3 and Athena. Worked on NLP tasks (sentiment analysis, text classification) and collaborated with engineers and marketers to translate requirements into scalable data and ML solutions. Produced documentation covering data flows, governance, and operational processes.