Nishant Pandey

• Developed and optimised pipelines using Apache Spark (Scala) and Delta Lake to compute campaign KPIs and custom user attributes, improving user segmentation and driving a 20% increase in user engagement. • Built a rules-based attribute engine consuming real-time Kafka events, automating 90% attribute creation and syncing data to H-Base and ElasticSearch, reducing manual effort by 60%. • Optimised push notification pipelines using Scala Futures for parallel execution, enabling delivery of millions of personalised messages in 60% less time and boosting user engagement by 30%. • Designed and maintained ETL pipelines across multiple CRM campaign channels, supporting customised rewards and notifications for 10 mobile applications, including 2 real-money gaming platforms. • Implemented Apache NiFi pipelines to ingest data from Greenplum and Amazon S3 into HDFS, ensuring reliable data availability for downstream analytics and batch processing. • Engineered real-time Spark (Scala) pipelines to process Kafka events into Delta Lake with optimised partitioning, achieving 40% faster query performance on large-scale datasets.