Description

• Collaborate with stakeholders to devise solutions and designs for upcoming projects associated with any 360(SMART DATA) expansion initiatives.
• Evaluate project requirements and develop architecture designs that support the specific needs of these regions and expansion efforts.
• Ensure that solutions are scalable and adaptable to diverse business environments.
• Evaluate current manual data processes to uncover opportunities for automation within the Smart Data Management ecosystem.
• Develop optimized automated data pipelines and workflows to streamline data integration, transformation, and availability.
• Ensure efficient monitoring and notification systems for data pipeline success or failure to enhance operational efficiency.
• Create a common, flexible, and scalable data framework applicable to various data projects and analytics initiatives.
• Establish standardized data components, design patterns, and methodologies to enhance data engineering project delivery speed and efficiency.
• Collaborate with data analysts and scientists to adapt the framework to meet unique data requirements while maintaining scalability.
• Leverage extensive experience to enforce data engineering best practices throughout the development lifecycle.
• Ensure that data solutions are standardized, maintainable, and reliable for long-term operational support.
• Conduct code reviews and provide technical guidance to data engineering teams on best practices.
• Assess existing data processes and workflows to identify limitations and areas for improvement.
• Recommend actionable solutions to optimize data efficiency, enhance data quality, and reduce operational costs.
• Promote a culture of innovation by exploring emerging technologies and methodologies that can enhance data engineering practices.
• Propose and pilot innovative data solutions that challenge conventional data management approaches and enable new ways of leveraging data.
• Collaborate with cross-functional teams to drive the adoption of innovative technologies across the data landscape.
• Continuously research and stay updated on the latest data engineering technologies and industry trends.
• Evaluate and integrate suitable technologies into existing data solutions to enhance organizational competitiveness and adaptability.