Sreedhar Reddy

Data Engineer

Bengaluru, Karnataka, India15 yrs 4 mos experience

Key Highlights

15+ years in IT with Azure Cloud specialization
Expert in building scalable data pipelines
Certified Azure and Databricks Data Engineer

Stackforce AI infers this person is a Data Engineering expert in Healthcare and Cloud technologies.

Contact

beingsreedhar@gmail.com LinkedIn

Skills

Core Skills

Azure Data FactoryAzure Databricks

Other Skills

PySparkAzure Logic AppsPython (Programming Language)SQLOracle DatabaseOracle ApplicationsPL/SQLOracle Data Integrator (ODI)UnixData WarehousingBusiness IntelligenceExtract, Transform, Load (ETL)Oracle BI AppsOracle Data IntegratorOracle Business Intelligence

About

With over 15 years in the IT industry and 4+ years specializing in Azure Cloud, and Databricks, I have a solid track record in building efficient, scalable data pipelines. My expertise spans across designing robust data pipelines, implementing data transformations using PySpark and SQL, and driving impactful data strategies for business value. Certified as a Microsoft Azure Data Engineer Associate and a Databricks Certified Data Engineer Professional, I am passionate about leveraging advanced cloud technologies to unlock the power of data. I thrive on solving complex data challenges, optimizing workflows, and enabling data-driven decision-making through reliable, high-quality engineering.

Experience

Persistent systems

Architect

Jun 2024 – Present · 1 yr 10 mos · Bengaluru, Karnataka, India · Hybrid

Client: United Health Group (OPTUM)
Project Name: DEP Implementations
Built and automated data pipelines in Azure Data Factory (ADF) with robust error handling and orchestration for large-scale ingestion workflows.
Automated Logic Apps auto scaling and retries using ADF Web Activities and Azure Automation Runbooks, reducing failures and ensuring cost-efficient operations.
Designed a Databricks PySpark solution to process 20GB+ EBCDIC files, splitting by record types (C10, C20, D10) and handling headers/trailers with configurable chunking logic.
Built a dynamic ADF automation pipeline to trigger child pipelines using Web Activities, passing batch IDs from Databricks notebook outputs into downstream pipelines for seamless processing.
Implemented secure data access by configuring private endpoints, VNET integration, and DNS resolution for Logic Apps and Storage Accounts.
Developed Python utilities for parsing, validation, and orchestration, enabling reusable and modular automation for data workflows.
Optimized SQL queries and PySpark transformations for reconciliation and reporting, improving performance and accuracy.
Partnered with architects to deliver fault-tolerant, scalable, and secure Azure data solutions while staying hands-on in development.