Balaji Jayapal, Director of Data Engineering at Dropbox, is a seasoned professional with extensive experience in data engineering, data platform, business intelligence, and machine learning. With a proven track record of delivering significant data-driven results, Balaji has developed and executed data roadmaps that have enabled swift decision-making and operational efficiency for multiple large enterprises and startups. He specializes in leading complicated programs with competing priorities and building and scaling engineering and platform teams.

Balaji began his career at MicroStrategy, where he learned about the data landscape and how customers leverage their data platforms. From there, he progressed to Engineering management as a Senior Manager at Electronic Arts (EA), and subsequently received multiple promotions and new work experiences that led him to become the Director of Data Engineering at Dropbox. In his current role, Balaji is building Dropbox's Data Engineering practice from the ground up and standardizing Engineering practices across the whole enterprise.

Some of Balaji's accomplishments include building the Data Engineering practice at Dropbox from the ground up by developing new business processes and benchmarks, and hiring 15+ Data Engineers in one year, resulting in achieving the Aim Higher culture award. He also implemented methodologies that enabled the operationalizing of machine learning models while processing data from 700M+ users, resulting in increasing ARR by several million dollars, reducing churn by 10%+, and improving conversion rate by over 25%. Balaji re-architected Dropbox's data logging, ingestion, storage, and processing systems, resulting in improving both platform and data reliability by 5X and reducing the number of SEVs from 10+ to less than two. He also implemented a new data monitoring framework and tools to track 200+ data pipelines, which helps the data teams reduce KTLO by 30%, reduce pipeline problems by 50%+, and improve data quality.

Balaji's skills include data engineering, data science, business intelligence, data infrastructure, data models, data pipelines, data warehousing, project management, system reliability, data telemetry, machine learning, data lineage, SQL, Python, AWS, Azure, Hadoop, Hive, HDFS, Snowflake, Airflow, Tableau, and Superset.