• Design and implement a scalable, robust, and consistent data architecture, aligned with business objectives, that supports both operational and analytical needs. • Own and support data pipelines integrating data sourced across multiple systems. Deliver a highly available data platform leveraged by business users across the enterprise. • Partner with engineering teams to integrate AI and machine learning models into the data architecture, enabling predictive analytics, automation, and improved decision-making capabilities. • Work closely with business and project stakeholders to deliver data solutions for a diverse set of problems. • Help ensure security, privacy, and compliance for all data assets and workflows. • Create comprehensive documentation for data processes, structures, and pipelines. Provide training and support to ensure best practices are followed throughout the organization. Design and develop data pipelines for data collection, analysis, and related functions. • Establish and enforce data governance standards, ensuring data consistency, accuracy, and integrity across all systems and departments. • Provide thought leadership for technical architecture design, development, and deployment to support enterprise-wide data needs.

• Plays a leadership role in the analysis, design, development, documentation, implementation and maintenance support of data management platforms for all areas of the Bank through the project life-cycle and across single or multiple systems. • Investigates and trains other staff on identification of program errors, definition of problems, debugging, unit testing and implementation of changes. • Trains junior staff in data engineering concepts, standards, and best practices. • Provides training, assistance, and guidance for maintenance support of existing Bank applications, data management and analytics platforms and related package products as directed. • Interfaces with other data engineering, and data science teams to implement bank initiatives. • Demonstrates technical leadership by staying abreast of emerging data engineering technologies and implementing industry best practices. • Lead developing data solutions, provide subject matter expertise utilizing SQL Server, .net and Microsoft Azure technology stack and other relevant technologies in coordination with Enterprise Architects. • Actively leads process improvement initiatives. • At times, may be responsible to lead process improvement initiatives. • Reports task progress to applicable stakeholders. • Performs other duties as assigned.

• Work closely with team members on analysis, design, development, documentation, implementation and support of data design and modeling needs throughout the SDLC. • Provide vision, direction and leadership to improve the Data Architecture of the client. • Work collaboratively with staff to provide architectural guidance and leadership in the design and implementation of all active projects. • Lead design and technical problem-solving meetings focusing on technologies, proof of concept activities, and models related to data management. • Maintain a clear understanding of client’s strategic initiatives and requirements for shared data assets to evaluate current and emerging technologies and industry trends. • Collect, analyze and manipulate complex information in order to summarize and present recommendations that impact business decisions.

• Design, implement, and maintain Azure Data Factory pipelines for ingestion, transformation, and orchestration of data workflows. • Consume and process data from various structured and unstructured internal and external data sources. • Build scalable data models to support reporting and analytics use cases. • Develop and maintain dashboards and reports using BI tools (e.g., Power BI, Tableau, or similar). • Identify, troubleshoot, and resolve current data issues, ensuring data accuracy, reliability, and performance. • Implement best practices for data governance, security, quality, and documentation. • Collaborate closely with data analysts, data scientists, engineers, and business stakeholders. • Optimize data processes for performance, cost-efficiency, and scalability. • Monitor, test, and validate the performance of data flows and pipelines.