---
title: "Anirudh Bihani - Data Engineer in Bengaluru | Tal"
description: "Meet Anirudh Bihani, Data Engineer @ M2P Fintech | PySpark · AWS · Azure · Kafka · ETL · SQL · API Integration · Data Migration | Turning Raw Data Into Business Intelligence based in Bengaluru. AI stack includes cursor, hugging face, and langchain."
image: "https://tal.club/meet/anirudh/share-image"
---

# Anirudh Bihani on Tal

## Headline

Data Engineer @ M2P Fintech \| PySpark · AWS · Azure · Kafka · ETL · SQL · API Integration · Data Migration \| Turning Raw Data Into Business Intelligence

## About

I build data pipelines that people actually rely on\. Data Engineer with 1\.5\+ years of production experience across fintech and automotive tech\. I specialise in ETL pipeline development, PySpark optimisation, and incremental data loading — working with Python, SQL, AWS, Azure, and Kafka to move and transform data at scale\. At M2P Fintech I've built ingestion frameworks processing 10GB\+ daily, applied Spark optimisation techniques \(partitioning, salting, broadcast joins\) to eliminate bottlenecks, and implemented Kafka\-based incremental loads that replaced expensive full reloads\. I care about clean, reliable, analytics\-ready data — and I'm actively looking for my next Data Engineer role where I can go deeper on distributed systems and large\-scale pipeline architecture\. 📩 anirudhbihani27@gmail\.com

## Location

Bengaluru

## AI stack

cursor

hugging face

langchain

ollama

## Work experience

Data Engineer at M2P Fintech, Sep 2024 \- Present · 1 yr 10 mos, On\-site, Building and optimising end\-to\-end data pipelines that process 10GB\+ daily across multiple fintech source systems — delivering clean, analytics\-ready data for business reporting and decision\-making\. \- Architected scalable ETL and data migration pipelines using PySpark, Python, SQL, and Pentaho — supporting batch and incremental ingestion workflows across 5\+ source systems, reducing data latency by replacing full reloads with API\-based incremental loading via Kafka\. \- Optimised Spark job performance through data partitioning, salting, broadcast joins, and skew mitigation — stabilising long\-running jobs and eliminating processing bottlenecks in high\-volume production environments\. \- Managed analytics\-ready datasets on Amazon S3 using query\-optimised, partitioned Parquet storage — enabling downstream reporting teams to run faster, more reliable queries on structured financial data\. \- Automated CI/CD pipeline deployments and orchestrated data workflows using AWS services — ensuring high availability, fault tolerance, and consistent environment parity across dev, staging, and production\.

Software Engineer Intern at KPIT, Jun 2023 \- Oct 2023 · 5 mos, Enhanced proficiency in Modern C\+\+ through structured training and project work\. Developed optimized, resilient code using advanced C\+\+ features\.

Trainee at KPIT, Jun 2023 \- Sep 2023 · 4 mos, Pune, Maharashtra, India

## Education

Bachelor of Technology \- BTech, Information Technology, MIT ADT University

Master of Information Technology, MIT ADT University

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

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