Sachin Patel Logo Image
Sachin Patel

Hi, My name is Sachin Patel

I am a Data Engineer based in Mumbai, India having with 5+ years of experience in designing and deploying data pipelines and warehouses using Python, SQL, and cloud platforms.

About Me Azure Data Engineer with 6+ years of experience in IT, specializing in Azure Cloud Data Solutions, ETL development, and Data Warehousing. Proven expertise in orchestrating large-scale data workflows, optimizing performance, and driving automation initiatives.

Professional Summary

Hey! I'm Sachin Patel, an Azure Data Engineer based in Mumbai, India, with 6+ years of experience in IT, specializing in Azure Cloud Data Solutions, ETL development, and Data Warehousing. I hold a Master of Science in Computing (Data Analytics) from Dublin City University and have worked with leading organizations including Marsh McLennan and Capgemini.

Currently serving as an Azure Data Engineer at Marsh McLennan, I orchestrate end-to-end data workflows using Azure Data Factory and Databricks, streamlining Accounts, Contacts, Employee Information, Sales and Opportunities data processing. I lead a data integration team responsible for building pipelines and jobs that automate the flow of sales and marketing related data.

My expertise spans across Azure Data Factory, Azure Synapse Analytics, Microsoft Fabric, and Databricks for orchestrating large-scale data workflows. I have extensive experience with diverse data sources including Parquet files, JSON, SQL Server, Salesforce, and API calls, and have successfully reduced pipeline run times by 80% through optimization.

I'm skilled in data modeling including Fact & Dimension tables, Slowly Changing Dimensions (SCD), and Star & Snowflake schemas. My experience includes working with Oracle PL/SQL, MS SQL Server, and Azure Synapse SQL, with a strong focus on performance optimization, monitoring, automation, and production support.

Key Achievements

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  • Pipeline Optimization at Marsh McLennan: Restructured existing pipelines, reducing pipeline run time by 80% and improving overall data processing efficiency
  • Automated Monitoring Solutions: Instituted automated monitoring using Azure Monitor and Application Insights, slashing incident response times by 50% and bolstering system reliability
  • Data Integration Leadership: Leading a data integration team at Marsh McLennan, responsible for building pipelines and jobs that automate sales and marketing data flow
  • Multi-Source Data Integration: Successfully integrated data from Dataverse to Azure Synapse SQL table and Oracle table using Synapse Pipelines and Dataflows
  • Diverse Data Source Expertise: Worked with various data sources including Parquet files, JSON, SQL Server data, Salesforce, and API calls for comprehensive data solutions
  • Production Support Excellence: 4+ years of experience in production support, process improvements, and performance optimization across multiple enterprise environments
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Technical Skills

Cloud & Data Integration

Databricks
Azure Data Factory
Azure Synapse Analytics
Microsoft Fabric
Microsoft CRM Dynamics
Azure DevOps

ETL & Data Warehousing

Informatica PowerCenter
SQL Server
Oracle Database
Dataflows
Azure Synapse Pipelines
ETL/ELT Development

Database Technologies

Oracle PL/SQL
MS SQL Server
Azure Synapse SQL
Data Warehousing
Performance Optimization

Data Modeling

Fact & Dimension Tables
Slowly Changing Dimensions (SCD)
Star Schema
Snowflake Schema
Data Architecture

Data Sources & Formats

Delta Lake Tables
Parquet Files
JSON
APIs
Salesforce
Flat Files

Operations & Support

Performance Optimization
Monitoring & Automation
Production Support
Process Improvements
Azure Monitor
Application Insights

Data Engineering Methodologies

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I follow industry best practices and modern data engineering methodologies to deliver robust, scalable solutions:

  • Data Pipeline Architecture: Design and implement end-to-end data pipelines using Azure Data Factory for orchestration, Databricks for processing, and Azure SQL for storage
  • ETL/ELT Optimization: Build efficient Extract, Transform, Load processes with Python and PySpark, handling both batch and real-time data processing
  • Data Quality & Monitoring: Implement comprehensive data validation, quality checks, and monitoring systems to ensure data accuracy and pipeline reliability
  • Cloud-Native Solutions: Leverage Azure cloud services for scalable, cost-effective data solutions with proper security and compliance measures
  • Performance Tuning: Optimize SQL queries, PySpark jobs, and ADF pipelines for maximum performance and cost efficiency

Education Academic background in Data Analytics and Computer Engineering

Master of Science - Computing (Data Analytics)

September 2022 - September 2023
Dublin City University
Dublin, Ireland

Advanced studies in data analytics, machine learning, and computational methods for data processing and analysis.

Bachelor of Engineering (BE) - Computer Engineering

July 2014 - July 2018
Jawahar Education Society's A.C. Patil College of Engineering
Navi Mumbai, Maharashtra, India

Comprehensive foundation in computer engineering, software development, and system design.

Projects I've contributed to diverse projects ranging from data pipeline development in Azure to machine learning models for image classification and financial risk analysis.

Timezone Converter Website Screenshot

Timezone Converter

A responsive web application for converting time between different timezones with real-time updates and intuitive UI.

Software Screenshot

IndiaFashion

Built a Machine learning image classification model for classification of Indian Ethnic Apparel.

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Software Screenshot

Etsy Multiclass Classification Model

Built a Machine Learning multiclass classification model for classifying different Products on the basis of top category wise, sub-category wise, primary color wise.

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Software Screenshot

Loan Application Approval System

Built a binary classification model to approve loan or reject on the basis whether the applicant will default or not.

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Contact Feel free to reach out to me by filling the below form: