Chandra Sekhar P

About Chandra Sekhar P

4+ years as Data Engineer focused on building scalable, high-performance data platforms and distributed data systems in cloud environments.I specialize in designing and optimizing large-scale data pipelines that handle high-volume, high-velocity data, enabling reliable and efficient data processing for analytics and business-critical use cases.My experience spans across AWS, Databricks, and GCP, working with modern data stacks to:• Build and scale robust end-to-end data pipelines• Optimize performance, reliability, and cost of data workflows• Develop efficient ETL/ELT processes for large-scale datasets• Implement CI/CD for automated, production-grade data systemsI have strong expertise in Python, SQL, Spark, and PySpark, with a deep understanding of distributed data processing and data architecture.I am particularly interested in:• Large-scale data platform engineering• Distributed systems and data infrastructure• Cloud-native architectures• Performance optimization at scaleDriven by solving complex data challenges, I focus on building reliable, scalable, and efficient data systems that power data-driven decisions.

Experience

1. Consultant

   Deloitte

   Mar 2025 — Present · IN

   ENTERPRISE DATA HUB – AGCO Agricultural Company (Mar 2025 – Present)Designed and maintained scalable ETL pipelines using AWS Glue and Glue Workflows for enterprise data integration.Ingested data from SAP, Salesforce, Feedscan, and Qlik into S3 using AWS AppFlow and processed it into curated Redshift layers.Built and optimized Redshift views to support BI dashboards and business reporting.Improved performance and reduced load time by migrating from Glue Crawlers to AppFlow, cutting processing time from 4–5 hours to under 2 hours.Achieved 40% cost reduction through pipeline optimization while ensuring secure, governed, and high-quality data delivery.

Education

• SRKR Engineering College

  Bachelor of Technology - BTech, Electrical and Electronics Engineering

  2017