Adedamola Onabanjo

Introduction​

Building scalable data pipelines in a fast-growing fintech can feel like fixing a bike while riding it. You must keep insights flowing even as data volumes explode. At Kuda (a Nigerian neo-bank), we faced this problem as our user base surged. Traditional batch ETL (rebuilding entire tables each run) started to buckle; pipelines took hours, and costs ballooned. We needed to keep data fresh without reprocessing everything. Our solution was to leverage dbt’s incremental models, which process only new or changed records. This dramatically cut run times and curbed our BigQuery costs, letting us scale efficiently.