Pranay Shridhar

I’m a Product Data Engineer with 3+ years of progressive experience at Barclays, where I build cloud-native, high-performance data platforms for highly regulated financial services. I’m known for turning complex data problems into reliable, measurable outcomes—focused on performance optimization, intelligent automation, and operational simplicity.

At Barclays, I led the migration of a legacy ERA process to AWS using Airflow DAG orchestration, improving system uptime by 25% and reducing latency by 40%. I also engineered a customer income selection and filtration system that processes 19M records, cutting data processing time and operational workload by 47%.

I automate pipelines end-to-end, including Dockerized AWS Cloud-Formation templates for PySpark and AWS Glue ETL workflows—reducing setup time by over 70%. I’ve optimized memory-intensive Spark workloads (500K+ records within a strict 2 GB limit) and streamlined DynamoDB ingestion of 8M records by tuning AWS Glue and partitioning, reducing runtime from 12 hours to 33 minutes.

I bring engineering depth to downstream integration by developing custom Spark and Java modules for a Decision Engine, reducing manual file generation tasks by 90%. Before my current role, I redesigned high-performance Big Data pipelines across 15+ Hadoop sources (throughput +30%) and built reusable Python/Shell automation that reduced manual ETL effort by 80%, while modernizing Ab Initio mainframes with Airflow-driven workflows.