---
title: "Sreeraj Mutha - Data Engineer internship in Bengaluru | Tal"
description: "Meet Sreeraj Mutha, Data Engineer Intern @ Nielsen | PySpark, AWS, Airflow, Kafka | B.Tech CSE ’26 based in Bengaluru. AI stack includes hugging face, langchain, and ollama."
image: "https://tal.club/meet/sreeraj/share-image"
---

# Sreeraj Mutha on Tal

## Headline

Data Engineer Intern @ Nielsen \| PySpark, AWS, Airflow, Kafka \| B\.Tech CSE ’26

## About

Data Engineer building production\-grade pipelines at Nielsen\. I work with PySpark, AWS \(EMR, EKS, S3, Step Functions\), Airflow, and Grafana — migrating legacy ETL systems to modern cloud\-native architectures\. What I've done: Migrated 15\+ Talend jobs to PySpark, built pipelines handling 2M\+ records/day, designed data quality frameworks catching 95% of anomalies, and reduced infrastructure costs by 30%\. Tech: Python \| SQL \| PySpark \| AWS \| Airflow \| Docker \| Kubernetes \| Grafana Open to full\-time Data Engineer / Backend Engineer roles starting mid\-2026\. Let's connect\.

## Location

Bengaluru

## AI stack

hugging face

langchain

ollama

openai

## Work experience

Data Engineer internship at Nielsen, Mar 2025 \- Present · 1 yr 4 mos, • Migrating 15\+ legacy Talend ETL jobs to PySpark on AWS EMR on EKS, reverse\-engineering undocumented workflows and reducing infrastructure costs by 30% and job runtime by 45%\. • Engineered batch data processing pipelines handling 2M\+ records daily across 3 input sources, implementing partitioning and caching strategies that improved Spark job performance by 40%\. • Orchestrated end\-to\-end pipeline execution using AWS Step Functions with 5\-stage state machines, retry logic, and error handling, achieving 99\.5% daily run reliability\. • Built real\-time monitoring with Grafana dashboards tracking 12\+ pipeline metrics and configured Airflow DAGs for scheduled execution and alerting across S3 data lake layers\. • Developed a data quality framework analyzing 4 months of production data across 20\+ columns, designing detection strategies for gradual drift vs sudden drops that caught 95% of anomalies pre\-production\. • Collaborated with a team of 8 engineers to map Talend workflow logic to PySpark transformations, documenting 10\+ migration patterns adopted as team\-wide best practices\.

## Education

Bachelor of Technology \- BTech, Computer Engineering, Lovely Professional University

[View the original profile](https://tal.club/meet/sreeraj)

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