---
title: "Bhargav Reddy Kummetha - Data Engineer in Halethorpe | Tal"
description: "Meet Bhargav Reddy Kummetha, Data Engineer | Building Scalable ETL Pipelines & ML Models | Driving Fraud Reduction, Churn Prediction & Data-Driven Insights | AWS • Databricks • Spark based in Halethorpe. AI stack includes codex, cursor, and opencode."
image: "https://tal.club/meet/bhargav-reddy-kummetha/share-image"
---

# Bhargav Reddy Kummetha on Tal

## Headline

Data Engineer \| Building Scalable ETL Pipelines & ML Models \| Driving Fraud Reduction, Churn Prediction & Data\-Driven Insights \| AWS • Databricks • Spark

## About

As a Data Engineer at Databricks, I specialize in building end\-to\-end ETL pipelines using Databricks \(PySpark, Delta Lake\), Python, SQL, and Java\. Collaborating with analytics and data science teams, I focus on creating efficient workflows, automating processes with Apache Airflow, and ensuring accurate data reporting that supports $5M\+ in monthly revenue\. I recently completed my Master of Professional Studies in Data Science at the University of Maryland, Baltimore County\. With 4\+ years of experience in Data Engineering and Machine Learning, I specialize in building scalable data pipelines, cloud\-based data platforms, and ML\-driven solutions using Python, SQL, Spark, Databricks, MLflow, Scikit\-learn, and PyTorch\. I am passionate about transforming data into actionable insights that drive business value and operational excellence\.

## Location

Halethorpe

## AI stack

codex

cursor

opencode

## Work experience

Data Engineer at Databricks, Jan 2025 \- Present · 1 yr 8 mos, United States, Engineer end\-to\-end ETL pipelines in Databricks \(PySpark, Delta Lake\) using Python, SQL, and Java, integrating 5\+ data sources to improve pipeline efficiency and provide clean datasets for analytics teams\. Automate workflows with Apache Airflow and CI/CD pipelines following DevOps best practices, reducing manual processing by 60% and ensuring $5M\+ monthly revenue data is accurate and available for reporting\. Collaborated with Data Scientists to maintain a feature store for fraud detection and churn prediction models, applying ML concepts and ML libraries \(Scikit\-learn, PyTorch, XGBoost, R\), reusing 20\+ features to accelerate model training cycles\. Deploy and monitor ML models using Databricks MLflow, implementing cloud computing solutions for model hosting, experiment tracking, and version control, cutting deployment time from weeks to days\. Optimize queries on AWS S3 with Delta Lake, leveraging Big Data technologies \(Spark, Delta Live Tables\) and C\+\+/Python for performance improvements, reducing reporting latency for $1M\+ quarterly transactions\. Delivered interactive Tableau and Databricks SQL dashboards combining data analysis, statistics, and visualization techniques, tracking KPIs like churn rate, fraud risk, and sales conversion, improving business decision\-making speed by 30%\.

Machine Learning Engineer at HCLTech, Jan 2020 \- Jul 2023 · 3 yrs 7 mos, Hyderabad, Telangana, India, Diagnosed data\-quality and labeling issues across finance and healthcare datasets, implemented ETL pipelines in Python, SQL, and Spark to standardize 5M\+ records, lowering fraud\-detection false positives and safeguarding $1M in transactions\. Developed end\-to\-end ML models using Scikit\-learn, TensorFlow, and PyTorch, applying advanced feature engineering and data warehousing techniques to boost predictive accuracy across credit\-risk and retail churn projects\. Designed NLP pipelines with Hugging Face Transformers to analyze unstructured claims and support\-ticket data, automating classification that reduced manual triage work by 40% in healthcare and retail client operations\. Deployed ML solutions to production on AWS SageMaker, Azure ML, and GCP Vertex AI, leveraging Docker, Kubernetes, and Kubeflow for containerized scaling and automated retraining workflows\. Implemented MLOps best practices with MLflow, Airflow, and CI/CD pipelines, establishing experiment tracking, reproducibility, and automated deployment cycles that cut release time from weeks to 72 hours\. Set up real\-time monitoring and logging \(Prometheus, Grafana\) to track drift and performance degradation, ensuring SLA compliance for mission\-critical fraud and churn models\. Collaborated with domain experts across finance, retail, and healthcare to translate business needs into production\-ready ML solutions, presenting insights to leadership and mentoring junior engineers on best practices\.

## Education

Master's degree, Data science, University of Maryland Baltimore County

Bachelor's degree, Computer Science, kalasalingam University

[View the original profile](https://tal.club/meet/bhargav-reddy-kummetha)

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