---
title: "Doddesh Makkalageri - Data Engineer in Karnataka | Tal"
description: "Meet Doddesh Makkalageri, immediate joiner , having 4+ years experinece as a data engineer... based in Karnataka."
image: "https://tal.club/meet/doddesh-makkalageri/share-image"
---

# Doddesh Makkalageri on Tal

## Headline

immediate joiner , having 4\+ years experinece as a data engineer\.\.\.

## Location

Karnataka

## Work experience

Data Engineer at Asics Technologies, May 2024 \- Present · 2 yrs 2 mos, Hyderabad, Telangana, India, Expertise in architecting and optimizing large\-scale data solutions across diverse industries\. Proficient in designing robust data pipelines using Hadoop, Apache Spark and Hive with a strong command of distributed computing principles\. ❖ Understanding of data modelling, partitioning strategies, and performance tuning for high\-throughput systems\. ❖ Collaborated with cross\-functional teams including Data Scientists, Analysts, and DevOps to deliver end\-toend data solutions\. ❖ Skilled in leveraging Hive partitioning, bucketing, indexing, and caching features to improve query performance and reduce data processing overhead\. ❖ Managed Hadoop clusters using Cloudera distributions, including configuration, monitoring, and troubleshooting ❖ Developed Hive queries for data summarization, ad\-hoc analysis, and reporting on large datasets\. ❖ Proficient in using Apache Sqoop to import and export data between Hadoop Distributed File System \(HDFS\) and relational databases like MySQL, Oracle, and PostgreSQL\. ❖ Optimized Sqoop performance using parallel imports, split\-by columns, and compression techniques ❖ Integrated Hive with Apache Sqoop for seamless data ingestion from relational databases into Hadoop\. ❖ Created DAG templates to standardize job orchestration across multiple Spark use cases\. ❖ Used AWS EMR to run distributed Spark and Hadoop jobs, tuning cluster configurations for performance and cost optimization ❖ Developed Spark jobs on AWS EMR for large\-scale data stored in AWS S3 buckets\. ❖ Optimized PySpark applications on AWS EMR by tuning shuffling and partitioning strategies\. ❖ Used AWS Step Functions to parallelize PySpark jobs for faster execution on AWS EMR clusters\.

Data Engineer at Amdocs, Dec 2021 \- Apr 2024 · 2 yrs 5 mos, Pune District, Maharashtra, India, Designed and developed DAGs in Apache Airflow for orchestrating complex Spark pipelines on AWS EMR\. ❖ Integrated Airflow with AWS services including S3, EMR, Lambda, and SNS for event\-driven workflows\. ❖ Tuned Airflow DAG execution timings and concurrency for optimal resource usage and parallelism\. ❖ Optimized Airflow worker pool configuration for scaling orchestration in large\-scale environments\. ❖ Proficient in handling hive partitions and buckets with respect to the business requirement\. ❖ Experience in handling hive schema evolution with avro file format ❖ Skilled in handling semi structured/serialised data processing using hive \(AVRO,PARQUET,ORC\) ❖ Experienced in efficiently using Hive managed and external table with respect to the business requirement\. ❖ Proficient in creating and managing Hive tables, including managed, external, and partitioned tables\. ❖ Ability to troubleshoot common issues with Hive tables, such as data skew, table corruption, and query optimization\.

## Education

Bachelor of Engineering \- BE, Mechanical Engineering, Visvesvaraya Technological University

Bachelor of Engineering \- BE, Mechanical Engineering, Visvesvaraya Technological University

[View the original profile](https://tal.club/meet/doddesh-makkalageri)

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