Real-Time Data Pipeline Optimization for Enhanced Payment Processing Insights

We are a mid-sized Payment Processing company looking to leverage real-time data analytics to optimize our transaction processing and fraud detection capabilities. The goal is to build a robust, scalable data pipeline that facilitates real-time insights into payment transactions. Our current system lacks the ability to process and analyze data in real-time, which limits our ability to promptly detect anomalies or trends in transaction data. We seek an experienced Data Engineer to design and implement a data architecture that integrates technologies such as Apache Kafka for event streaming, dbt for data transformation, and Snowflake or BigQuery for data warehousing. The project requires setting up automated workflows using Airflow and ensuring high data observability for proactive monitoring. An ideal solution will support our MLOps strategy to deploy machine learning models for advanced fraud detection and predictive analytics. The entire setup should be scalable to accommodate our growing data needs without compromising on speed or accuracy.