Pine59 manages large data pipelines for location intelligence, producing metrics hourly to quarterly. Their Daily Foot Traffic metric processes up to 14 million locations per job, running on Google Cloud with BigQuery and Managed Service for Apache Airflow orchestrated by Airflow 3. To handle increasing data volumes and machine learning workloads, Pine59 modernized their monorepo containing hundreds of Directed Acyclic Graphs (DAGs). This transition significantly improved their MLOps capabilities, developer workflow, and pipeline speed.They stress-tested production workloads against the new Managed Airflow (Gen 3) architecture and observed immediate improvements in processing speed, task scheduling, and stability. The migration optimized MLOps by integrating a dedicated Google Kubernetes Engine cluster for model inference, separating orchestration from heavy ML execution. Airflow 3's improved developer workflow and user interface were leveraged through custom plugins like "BigQuery Auto-linkify" and "DAG Run Configuration Search" for faster debugging and troubleshooting. A compatibility shim layer streamlined operator migration between Airflow versions.Quantifiable results include dramatically reduced queue latency for DAG runs, with tasks starting almost immediately. The Daily Foot Traffic pipeline's completion time decreased by nearly 32%, from 38 minutes to under 26 minutes. Pine59 now runs all production workloads on their Gen 3 instance, establishing a faster, more resilient foundation for future data and AI pipelines. This move allowed their engineering team to focus more on value delivery than infrastructure management.