Real-time Patient Data Integration for Enhanced Predictive Analytics in Digital Health

As a forward-thinking startup in the Digital Health industry, we are committed to revolutionizing patient care through advanced predictive analytics. We're embarking on a project to build a comprehensive data engineering solution that integrates real-time patient data from wearables, healthcare apps, and EMR systems. Our aim is to leverage this integrated data to provide healthcare providers with actionable insights that enhance decision-making and patient outcomes. The project involves creating a resilient data pipeline utilizing state-of-the-art technologies like Apache Kafka for real-time event streaming, Apache Spark for data processing, and dbt for data transformation. Snowflake and BigQuery will serve as the backbone for our data warehousing needs, ensuring scalability and efficiency. Additionally, Airflow will orchestrate the workflow, ensuring seamless execution and monitoring of data processes. We require a seasoned data engineer who can design this architecture to support real-time analytics and facilitate the integration with machine learning models (MLOps). The solution should also include data observability features to ensure data quality and integrity. This project is critical for our ability to provide predictive analytics to healthcare providers, directly impacting patient care quality.