Real-Time Data Pipeline Optimization for Financial Insights

As a burgeoning FinTech startup, our core mission is to deliver precise financial insights that empower clients to make informed decisions swiftly. Currently, our data processing pipelines are not optimized for real-time analytics, which impedes our ability to provide timely insights and affects our competitive edge. We seek a data engineering expert to revamp our data infrastructure, focusing on optimizing real-time pipelines. The project involves integrating Apache Kafka for efficient event streaming, leveraging Spark for real-time analytics, and ensuring seamless orchestration using Airflow. With an eye on the latest trends such as data mesh and MLOps, the goal is to create a robust and scalable architecture that supports complex data transformations and aggregations. Additionally, implementing data observability tools will be crucial for monitoring and maintaining data quality. To achieve this, we anticipate using a combination of dbt for data transformation, Snowflake or BigQuery for cloud data warehousing, and Databricks for unified analytics. The freelancer will collaborate with our data science team to ensure that the data pipelines align with our machine learning models' requirements, supporting real-time predictive analytics. The successful implementation of this project will significantly enhance our ability to provide accurate, timely insights, thus improving customer satisfaction and expanding our market share.