Real-Time Data Pipeline Optimization for Enhanced Subscription Insights

As a growing player in the subscription economy, we face the challenge of effectively managing and analyzing vast amounts of subscriber data in real time. Our current data infrastructure struggles to keep pace with the demand for rapid insights that can inform marketing strategies and improve customer retention. This project aims to overhaul our data pipeline architecture, implementing solutions that support real-time analytics and data observability. We are looking to incorporate technologies like Apache Kafka for event streaming, Spark for fast data processing, and Databricks for efficient data lake management. Additionally, tools such as dbt and Airflow will be used for data transformation and orchestration. Snowflake or BigQuery will serve as our data warehouse solution to store and analyze complex datasets. The outcome should be a robust and scalable data architecture that provides our teams with up-to-the-minute insights into subscriber engagement and preferences. The ideal freelancer will have experience in designing data architectures that embrace data mesh principles, ensuring data democratization across the organization. Furthermore, expertise in MLOps is desirable to facilitate the integration of machine learning models for predictive analytics. This project is critical to achieving our growth targets and maintaining a competitive edge in the rapidly evolving subscription market.