Real-time Audience Analytics Platform for Podcast Performance Optimization

As a fast-growing player in the Podcast & Radio industry, we aim to harness the power of real-time data analytics to transform how our podcast content is consumed and monetized. Currently, our data is scattered across various platforms, resulting in delayed insights into listener behaviors and engagement patterns. We seek an experienced data engineer to design and implement a comprehensive data architecture that supports real-time analytics and event streaming. The project involves setting up a robust data pipeline using technologies such as Apache Kafka for real-time data ingestion and Spark for stream processing. The solution will also utilize Airflow for orchestration, dbt for data transformation, and Snowflake or BigQuery for scalable storage and querying. An important aspect of the project is to ensure data observability and integrity, enabling us to quickly identify trends and adapt content strategies accordingly. The development of this platform is crucial for enhancing our content creators' ability to make data-driven decisions and ultimately improve listener engagement and retention.