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JayanthM16/README.md

Jayanth Mekala

Data Engineer | ETL/ELT pipelines | PySpark and Databricks | Azure, AWS and Snowflake

M.S. in Data Science, University of Massachusetts Dartmouth (May 2026). I build batch and streaming data pipelines, lakehouse tables and the data quality checks around them, mostly for insurance and healthcare data.

What I work with

Area Tools
Languages SQL, Python (pandas), PySpark, Spark SQL, Bash
Azure Data Factory, Databricks, ADLS Gen2, Delta Lake, Unity Catalog, Key Vault
AWS S3, Glue, Lambda, Athena, EMR, Kinesis, IAM
Warehousing and modeling Snowflake, SQL Server, dbt, Medallion Architecture, SCD Type 1/2
Orchestration and CI/CD Apache Airflow, GitHub Actions, Terraform
Analytics and ML Power BI, Tableau, PyTorch, Hugging Face Transformers

Projects

Project What it is Stack
youtube-trending-aws-pipeline Serverless data lake that turns raw YouTube trending CSV/JSON into partitioned Parquet analytics tables S3, Lambda, Glue (PySpark), Athena, QuickSight
ml-driftnet Master's project: fine-tuning GPT-2 Small on ML Q&A data and proving domain adaptation with five metrics PyTorch, Hugging Face Transformers

Contact

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  1. ML_LLM_Domain_Adaptation ML_LLM_Domain_Adaptation Public

    Fine-tuning GPT-2 Small on ML Q&A data and proving domain adaptation with a five-metric evaluation framework (41.1% lower held-out perplexity).

    Jupyter Notebook

  2. Youtube-Trending_Analysis-AWS_Pipeline Youtube-Trending_Analysis-AWS_Pipeline Public

    Serverless AWS data lake pipeline for YouTube trending data: S3, Lambda and Glue (PySpark) to partitioned Parquet, queried with Athena and QuickSight.

    Python