Real-Time Music Consumption Analytics Platform Development

As a rapidly growing music streaming service, we aim to harness the power of real-time data analytics to better understand our audience's listening behaviors. This project involves developing a robust data pipeline using Apache Kafka for event streaming, Spark for data processing, and Airflow for scheduling. The platform will integrate with Snowflake or BigQuery for data storage and utilize dbt for data transformations. Our objective is to create a seamless analytics flow that delivers insights on user engagement, popular tracks, and emerging trends. The solution should also include data observability features to ensure data quality and reliability. The ideal candidate will have experience with MLOps to support machine learning models that predict user preferences, contributing to a personalized listening experience. The project is crucial for enhancing user satisfaction and increasing market competitiveness.