Krishna Inapurapu
Verified Expert in Engineering
Data Engineer and Developer
Clarington, ON, Canada
Toptal member since November 15, 2024
With 15+ years of experience in the banking and automobile industries, Krishna specializes in designing and delivering enterprise-grade solutions using Java, Spring, and Azure data engineering. His expertise lies in architecting scalable, secure, and high-performance data infrastructures that drive innovation and operational efficiency in complex, regulated environments. Krishna excels in leveraging technology to solve challenging problems and enhance business outcomes.
Portfolio
Experience
- PySpark - 10 years
- Python - 10 years
- Java EE 8 - 10 years
- SQL - 10 years
- Azure Data Lake - 8 years
- Azure Databricks - 8 years
- Azure Synapse - 6 years
- Azure Event Hubs - 6 years
Work Experience
Senior BI and Data Architect
General Motors
- Designed efficient, cost-effective data solutions focusing on performance optimization for swift data processing. Worked skillfully on data governance and security, ensuring data management and processing compliance with government standards.
- Performed data transformation with Azure Databricks, using aggregations and Window functions. Streamed data processing for real-time analytics, employing SQL, Java, and Python skills for back-end integration and custom processing.
- Handled cloud solutions and migration with Azure for efficient development and deployment, utilizing Azure Functions, Data Factory, Databricks, Synapse Analytics, Event Hubs, Cosmos DB, Snowflake, Kubernetes Service, Docker, and Protocol Buffers.
Senior Data Engineer
TD Bank Group
- Migrated and developed data pipelines from on-prem to cloud systems.
- Worked on various projects within business banking, mortgages, online banking, and capital markets.
- Handled E2E processes, from requirements gathering to production implementation.
Project History
Subscription App with FastAPI, Azure Event Hubs, & Azure Databricks
Each request triggers an Azure Function, orchestrating the data pipeline for real-time processing. All data transformations occur in Azure Databricks notebooks, where PySpark is used to prepare and transform data according to business logic.
This architecture ensures scalability, efficient data handling, and a responsive user experience, enabling seamless and secure subscription management with robust back-end processing.
Enterprise Solutions with the Azure Ecosystem
Azure Data Factory manages ETL workflows, integrating data from various sources. Data is transformed in Azure Databricks using PySpark, while Azure Synapse Analytics serves as a scalable data warehouse for analytics. Unix scripting automates processes, and Java and Python enable custom activities and transformations, enhancing performance and flexibility.
This solution supports secure, efficient data processing and reporting, meeting high compliance standards in the financial sector.
Education
Bachelor's Degree in Computer Science
Jawaharlal Nehru Technological University Hyderabad - Hyderabad, Telangana, India
Certifications
Microsoft Certified: Azure Data Engineer
Microsoft
Skills
Libraries/APIs
PySpark
Tools
Application Servers, Jira, Confluence, Jenkins, Git, SourceTree, IBM Rational ClearCase, IBM Rational ClearQuest, SQL Server BI, Oozie, Autosys, Azure Kubernetes Service (AKS)
Languages
Java EE 8, SQL, Python, Java, Snowflake
Frameworks
JavaBeans, Spring, Spring Boot, Spark, Hadoop
Paradigms
DevOps, Azure DevOps, Microservices, Business Intelligence (BI)
Platforms
Azure Synapse, Azure Event Hubs, Unix, JBoss, IBM WebSphere, Azure, Oracle, Azure Functions, Azure Synapse Analytics, Databricks, Docker
Storage
Azure Cosmos DB, MySQL, IBM Db2, Apache Hive
Other
Data Engineering, Azure Data Lake, Azure Databricks, Azure Data Factory (ADF), DataOps, CI/CD Pipelines, Enterprise Java Beans (EJB) 3, Enterprise Java Beans (EJB), EJB 3, Cosmos, Programming, Cloud, Data Architecture, FastAPI
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