1. Building and Maintaining Data Pipelines
▪ Design and implement robust, scalable, and efficient data pipelines to process and transform raw data into actionable insights.
▪ Develop and optimize data extraction, transformation, and loading (ETL) processes using modern tools and platforms.
▪ Ensure seamless integration of diverse data sources, including structured and unstructured datasets.
2. Data Architecture Design
▪ Collaborate with stakeholders to define technical requirements for data storage, retrieval, and processing.
▪ Implement best practices for database design, data modeling, and performance optimization.
▪ Contribute to the development of enterprise-wide data strategy and architecture.
3. Cloud Data Integration
▪ Deploy and maintain cloud-based data solutions, with a preference for platforms like AWS, Azure, or GCP.
▪ Ensure efficient utilization of cloud resources to achieve cost-effectiveness and scalability.
▪ Understand and apply basic cloud governance and security principles.
4. Data Governance and Quality
▪ Establish data governance frameworks to ensure the reliability, security, and accuracy of data.
▪ Conduct regular data audits and validations to maintain data quality.
▪ Implement automated monitoring systems for anomaly detection and issue resolution.