• Design and implement scalable data ingestion pipelines to efficiently collect and process data from various sources. • Integrate data from different systems and platforms to create unified datasets for analysis and reporting. • Develop and maintain data storage solutions such as data lakes, data warehouses, and NoSQL databases. • Optimize data storage and retrieval mechanisms for performance, scalability, and cost-effectiveness. • Implement data processing workflows for cleaning, transforming, and enriching raw data into usable formats. • Apply data transformation techniques such as ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes. • Build software applications and APIs to expose data services and functionality to other systems and applications. • Establish monitoring and alerting mechanisms to track the health and performance of data pipelines and systems. • Document data engineering processes, architectures, and solutions to facilitate knowledge sharing and collaboration. • Collaborate with cross-functional teams including data scientists, analysts, and business stakeholders to understand requirements and deliver solutions.