Jayachandra Srinivas

About Jayachandra Srinivas

I’m a Data Engineer specializing in building scalable, secure, and analytics-ready data platforms on Azure, with hands-on experience in Python, PySpark, Azure Databricks, Azure Data Factory, and ADLS Gen2. I focus on designing robust ETL/ELT pipelines, implementing modern lakehouse architectures, and enabling data-driven decision-making. At Infosys, I worked on enterprise-scale data engineering solutions, contributing to the design and automation of end-to-end data pipelines. My responsibilities included ingesting data from relational sources, orchestrating workflows, performing distributed transformations using PySpark, and optimizing analytical layers to ensure performance, reliability, and data quality. My Fraud Risk Analysis Data Engineering project demonstrates my ability to architect a production-grade Azure Lakehouse using Bronze–Silver–Gold architecture. I implemented incremental ingestion from Azure SQL using ADF, leveraged Databricks Auto Loader and Delta Lake, applied data quality and normalization logic in Silver, and built rule-based fraud risk indicators in the Gold layer. The platform is fully governed using Unity Catalog and serves analytics through Databricks SQL and Power BI dashboards. I have strong hands-on experience with Azure Databricks, Azure Data Factory, ADLS Gen2, Azure SQL, Delta Lake, and Power BI, and I understand enterprise concerns such as data governance, security, orchestration, scalability, and cost optimization. Curious and growth-driven, I’m passionate about cloud-native data engineering, lakehouse architectures, real-time and batch data processing, and building reliable analytics platforms that scale with business needs.🤝 Let’s connect if you’re working on Azure data platforms, Databricks lakehouse solutions, or large-scale analytics pipelines — I’m eager to collaborate, contribute, and keep learning.

Experience

1. Data Engineer

   Infosys

   Nov 2022 — Present · Bengaluru, IN

   As a Data Engineer, I design, build and Optimise scalable, efficient data pipelines and ETL workflows to support analytics and reporting for Airline Data Engineering ProjectKey Responsibilities & Contributions->Designed and built end-to-end data engineering pipelines on Microsoft Azure to ingest daily airline booking, passenger, and flight operations data from relational databases into Azure Data Lake Storage Gen2 (ADLS)using Azure Data Factory (ADF)->Developed ADF pipelines with Copy, Lookup, ForEach, and control-flow activities to support incremental and scheduled data ingestion, ensuring scalable and reliable data movement into the Bronze layer in Parquet/Delta format->Implemented data transformation workflows in Azure Databricks using PySpark, applying data cleansing, validation, schema enforcement, and business rules to transform data from Bronze to Silver following the Medallion (Bronze–Silver–Gold) architecture->Built analytics-ready Gold Delta tables in Databricks to support insights such as booking trends, flight delay analysis, route performance, and operational metrics, optimized for reporting and downstream consumption->Orchestrated Databricks notebooks through Azure Data Factory, enabling automated end-to-end execution, monitoring, and failure handling, while delivering curated datasets to analytics teams via Databricks SQL and Power BI for business reporting.Tech Stack: Python,Databricks, PySpark, SQL, Azure (ADF, ADLS GEN2), Airflow, Git, Docker

Education

• Dr.B.R.Ambekar university srikakulam

  Bachelor of Engineering - BE, Mechanical Engineering

  2018 — 2022