Building a Scalable Real-time Data Pipeline for Enhanced Business Intelligence

As a fast-growing scale-up in the software development industry, we are experiencing an exponential increase in data volume and complexity. To maintain our competitive edge, we need to transition from batch processing to real-time data analytics. We are seeking a data engineering professional to design and implement a scalable real-time data pipeline using cutting-edge technologies like Apache Kafka for event streaming, Spark for data processing, and Airflow for orchestrating workflows. The project also involves setting up a data mesh architecture, enhancing data observability, and integrating with cloud data warehouses such as Snowflake or BigQuery. The successful implementation of this pipeline will enable real-time analytics, facilitating timely decision-making and optimizing operations across our product lines. The project is crucial for leveraging our data-driven strategies, improving customer experience, and supporting MLOps initiatives by providing faster and more reliable data for machine learning models. Moreover, this pipeline will enhance our ability to deliver personalized content and offers to our diverse user base instantly. The project duration is estimated at 8-12 weeks, with a budget ranging from $15,000 to $50,000, reflecting the complexity and strategic importance of this initiative. Given the medium to high urgency, we seek a freelancer with expertise in modern data engineering tools and technologies, who can deliver a solution that ensures data quality, scalability, and reliability.