Jeyasimhan  ·  Lead Azure Data Engineer  ·  15+ yrs

Principal
India15+ years experienceremote
Available within 48 hrs

About Jeyasimhan

Jeyasimhan is an experienced Azure Data Engineer and Technical Architect with a strong background in data engineering across multi-cloud platforms. He specializes in converting unstructured data into structured formats and has extensive experience with Azure Data Factory, Data Bricks, and Apache Spark. His expertise includes automation through scripting and managing ETL processes for various industries, including healthcare and retail.

Core expertise

AD
Azure Data Factory
devops
9/10
Python
language
9/10
AD
Azure Data Bricks
devops
8/10
Apache Spark
language
8/10
MySQL
database
8/10

Additional skills(27)

PythonNode.jsRedisRabbitMQGoogle CloudPERLMySQLAzure Data FactoryAzure SQL ServerUnix Shell Scripts

Why hire Jeyasimhan?

Production deploy authorityMentored 5+ juniorsLed ETL process implementations

Proven experience as a Technical Architect with 15 years in data engineering.

Strong multi-cloud expertise with Azure, Google Cloud, and AWS.

Demonstrated ownership in ETL processes and data migration.

Designed and configured cluster architectures for distributed data processing.

Automated error logging and created Dashboard Metrics for framework performance monitoring.

Successfully converted large sets of legacy and unstructured data into structured formats for analytics.

Led and implemented ETL processes for clinical and multi-cloud data, handling various file formats.

Developed and deployed web crawlers to collect millions of records from e-commerce platforms.

Designed and configured cluster architectures and node configurations for efficient data processing systems.

Project highlights(6)

Healthcare Data ETL – Data Engineer/Architect

Overview: Managed ETL processes for legacy clinical data, transforming it into a structured format. Responsibilities: Worked closely with the Data Analytics team and stakeholders to design the data conversion process. Wrote notebook scripts using Python to filter and ingest various file formats (.tgz, .csv, json) with multi-level structures.

Azure Data FactoryPythonAzure SQL Server

Key outcomes:

  • Successfully processed and transformed legacy clinical data into a structured format.

  • Designed and implemented ETL pipelines using Azure Data Factory.

E-commerce Data Crawler – Developer/Architect

Overview: Developed automation scripts (Web Crawler) to collect fashion industry data for trend analytics, adhering to GDPR policy. Responsibilities: Gathered client requirements and conducted requirement analysis for the web crawler. Performed ETL on large-scale data using Redis for caching and RabbitMQ for queuing.

Node.jsRedisRabbitMQ

Key outcomes:

  • Collected millions of records from e-commerce platforms using automation scripts.

  • Ensured GDPR compliance in data collection processes.

Legacy Billing System Conversion – Developer/Engineer

Overview: Converted legacy hard copy billing systems into digital solutions using Google Cloud. Responsibilities: Configured initial authentication setup using boto on local machines. Used Google Cloud to convert existing data into an Entity relationship format.

Google CloudPython

Key outcomes:

  • Successfully converted legacy hard copy bills to digital format on Google Cloud.

  • Automated file transactions and data conversion processes.

NATE FRAMEWORK – Engineer

NATE FRAMEWORK — PNate framework installation on Storage Servers + cluster architecture + node configuration + automated error logs + dashboard metrics. PERL + Python + Unix Shell + MySQL.

PERLMySQLPythonUnix Shell ScriptsUNIX

Key outcomes:

  • Successfully installed and configured the PNate framework across diverse storage servers.

  • Automated error logging and generated dashboard metrics for monitoring.

  • Ensured framework compatibility and qualification across partner server versions.

Green Images – Developer/Engineer

Green Images — US government legacy hard-copy billing → digital solutions via Google Cloud Storage + Data Store with automated file transactions. gsutil + boto + Shell Scripting.

PythongsutilbotoShell ScriptingGoogle Cloud StorageGoogle Cloud Data store

Key outcomes:

  • Successfully converted legacy hard copy bills to digital format on Google Cloud.

  • Automated file transactions and data conversion processes.

  • Configured secure authentication for cloud operations.

15+ years of industry experience

Government & Public Sector1 project
  • •Green Images— Developer/EngineerPython · gsutil · boto · Shell Scripting +2
HealthTech1 project
  • •Healthcare Data ETL— Data Engineer/ArchitectAzure Data Factory · Python · Azure SQL Server
Legal TechReported in resume
SaaS / B2B1 project
  • •E-commerce Data Crawler— Developer/ArchitectNode.js · Redis · RabbitMQ

Ready to work with Jeyasimhan?

Onboard within 48 hours. No long hiring cycles, no recruiter middleman.

At a Glance

LocationIndia
Experience15+ years
Work moderemote
Direct hirePossible
Start within48 hours
From$2,156/ month

Single contract. Billed in USD.

Typically responds within 4 business hours.

5-day replacement guarantee
48-hour onboarding, single invoice
Direct chat — no recruiter middleman

Top Skills

Azure Data Factory
9/10
Python
9/10
Azure Data Bricks
8/10
Apache Spark
8/10
MySQL
8/10
Seniority signals
Owns production deploysGreenfield architectSystem ownerCode reviewerMentor / leads juniors
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Technical skills assessed & verified
Background & identity checked
English communication verified
Ready to onboard in 48 hours

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Jeyasimhan

Azure Data Engineer