About

Sanni Heruwala is a highly skilled and experienced data engineer with over 6 years of experience in data engineering and analytics across various industries including media, cloud communications, financial technology, and health care. He has a proven track record of helping companies create data collection and processing tools for high volume, velocity, veracity, and variety. Sanni's expertise includes designing and building scalable, fault-tolerant products such as data platforms and ingestion frameworks, self-serve analytics and reporting systems, data lakes, and data warehouses on-premises or in the AWS ecosystem. He is an expert in delivering low-latency data processing systems and is skilled in system design, data modeling, ETL, stream processing, and product analytics. Sanni is capable of building data processing systems using diverse components, including Spark, HDFS, S3, Lambda, Hive, DynamoDB, and Redis. He capably defines analytics charters and helps organizations move towards data-driven decision making. Currently, Sanni is working as a Data Engineer at Razorpay, where he has worked on building a real-time streaming pipeline to build a replica of similar data getting ingested via the front-end. This enables data science and BA/PA teams to utilize the data and provide real-time dashboards. In his previous roles at Tivo and IQVIA, Sanni was responsible for building data pipelines using TV viewership data and handling healthcare data at a large scale, respectively. He has also generated certain metrics using Spark, Scala, and pushing them into reporting, created airflow schedules and its orchestration, and produced region-level stats by mixing historical and live data. Sanni holds a Bachelor of Technology degree in Computer Science from Gujarat Technological University, Ahmedabad. His technical skills include Data Engineering, Spark, AWS, HDFS, Redis, Data Scientist, ETL, Frontend, Apache Airflow, Scala, and Apache Hive. With 7.78 years of relevant experience, Sanni is a valuable asset to any organization looking to build scalable, fault-tolerant data processing systems.