·         Design, develop, and maintain scalable data pipelines for efficient data extraction, transformation, and loading (ETL) processes.

·         Architect and implement data storage solutions, including data warehouses, data lakes, and data marts, aligned with business needs.

·         Implement robust data quality checks and data cleansing techniques to ensure data accuracy and consistency.

·         Optimize data pipelines for performance, scalability, and cost-effectiveness.

·         Collaborate with data analysts and data scientists to understand data requirements and translate them into technical solutions.

·         Develop and maintain data security measures to ensure data privacy and regulatory compliance.

·         Automate data processing tasks using scripting languages (Python, Bash) and big data frameworks (Spark, Hadoop).

·         Monitor data pipelines and infrastructure for performance and troubleshoot any issues.

·         Stay up to date with the latest trends and technologies in data engineering, including cloud platforms (AWS, Azure, GCP).

·         Document data pipelines, processes, and data models for maintainability and knowledge sharing.

·         Contribute to the overall data governance strategy and best practices.