TAPP provides essential insights for hospitality venues and their FMCG suppliers: analysis of your venue/brand's performance, based on consumption patterns and benchmarking versus similar businesses nearby, so entrepreneurs can make better decisions, faster.My team and I are responsible for building a data platform that enables us to scale our fast-growing business. This includes data ingestion, data refinement, infrastructure, and internal tooling.Scaled TAPP’s data platform from processing a handful of locations to over 5,000 out-of-home outlets daily, integrating 20+ API vendors with an international team of 7. The platform processed 3B+ transactions, focusing on data enrichment through manual tooling and machine learning. Built on Apache Airflow, AWS/Azure, Django, k8s, and Snowflake, my hands-on contributions included debugging concurrency issues, scaling historical data loads from 3rd-party APIs to optimize pipeline performance, and making major architectural decisions. I played a key role in scaling the tech department by growing the team, structuring processes, and improving code maturity.Led a multidisciplinary team of 6 to develop a customer-facing product in collaboration with Microsoft & Heineken. The product provided bar owners with advice on pricing, assortment, and a data-driven timeline with tips and insights. I dedicated considerable time to pair programming and mentoring contractors, helping them quickly get up to speed and become productive members of the team. The tech stack included React for the frontend and Django Ninja, MySQL, Snowflake, and CubeJS for the backend.Led the implementation of a real-time analytics database (Apache Pinot) with a team of 3, from POC to migration, achieving subsecond response times on multi-billion row datasets and reducing infrastructure costs by 50%. Contributed to the code, including the query builder, ensuring efficient SQL execution from API payloads. Completed in 3 months with zero downtime.