The Senior Data Engineer is essential for the successful modernization of our data platforms and practices. The ideal candidate will have expertise in cloud technologies, data architectures and agile development principles. Key responsibilities include leading data architecture initiatives, supporting hybrid data integrations and implementing advanced analytics solutions. This role will play a crucial role in the following, (though not limited to: areas:
Data Platform Modernization: Manage, evaluate and enhance our existing data platforms, identifying opportunities to leverage emerging technologies and cloud-based solutions (Microsoft Azure preferred). By modernizing our data infrastructure, we can improve scalability, accessibility, turnaround times, and security, paving the way for advanced analytics and driving data-based insights.
Process Improvement and Automation: Contribute to the establishment of standards, and procedures for data management, integration, quality management, and process improvement. By documenting, and optimizing these processes, we will ensure data consistency, accuracy, and timeliness, enabling reliable decision-making across the organization.
Collaboration: As a point of contact for the Data Services Team, the Senior Data Engineer will collaborate with IT teams, business units, and senior management to drive change and foster a data-driven culture. They will provide expert guidance, informed research, and support, ensuring successful implementation and stakeholder engagement throughout the lifecycle of data projects. Excellent communication is required with the focus on proper techniques to translate the complicated technical solutions for the business.
Data Platform Management: Evaluate, enhance, and modernize Goodwin’s data platforms, including exploring emerging technologies and cloud-based solutions to improve scalability, accessibility, and security.
Process Optimization: Contribute to establishing, documenting, and optimizing standardized procedures for data management, data governance, integration, quality management, and lifecycle management to ensure data consistency, accuracy, and timeliness.
Stakeholder Collaboration: Collaborating with cross-functional teams, business units, and senior management to drive data-driven initiatives, foster a data-driven culture, and ensure alignment with organizational goals.
Performance and Innovation: Monitoring and optimizing the firm’s data platforms (Microsoft SQL Server on premises, and Microsoft Azure Cloud), data engineering processes, identifying areas for improvement, and driving innovation to enhance data processing efficiency, data quality, and overall system performance.
