• Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, or a related field.
• Minimum of 10 years of experience in data architecture, data management, or a related field, with at least 5 years in a leadership role.
• Proven experience in financial services or investment banking sectors, with deep understanding of financial products, regulatory requirements, and market dynamics.
• Strong proficiency in data architecture frameworks, data modeling, and data governance practices.
• Experience with cloud data platforms (e.g., AWS, Azure, Google Cloud), big data technologies (e.g., Hadoop, Spark), and database management systems (e.g., Oracle, SQL Server, NoSQL).
• Familiarity with data integration tools (e.g., Informatica, Talend), ETL processes, and API-based data integration.
• Knowledge of AI/ML and advanced analytics tools (e.g., TensorFlow, PyTorch, Tableau, Power BI) is a plus.
• Proven ability to lead cross-functional teams and manage complex projects.
• Strong communication and interpersonal skills, with the ability to influence senior stakeholders and present complex data concepts in business-friendly language.
• Relevant certifications such as TOGAF, CDMP, or DAMA are desirable.

• Define and lead the data architecture strategy, ensuring alignment with the company's overall business strategy, regulatory requirements, and technological advancements.
• Develop and maintain an enterprise data architecture blueprint, including data models, data flows, and integration patterns that support the company's financial products and services.
• Establish and enforce data governance frameworks and ensure compliance with relevant data regulations, such as GDPR, CCPA, and industry-specific standards.
• Evaluate, select, and implement cutting-edge data technologies, tools, and platforms, including cloud-based data solutions, big data platforms, and AI/ML capabilities.
• Lead the integration of disparate data sources, ensuring data quality, consistency, and availability across the organization. Design ETL processes and data pipelines to support real-time and batch data processing.
• Work closely with business leaders, data scientists, data engineers, and other stakeholders to understand data needs and translate them into actionable data architecture plans.
• Build, mentor, and lead a high-performing team of data architects and engineers. Foster a culture of continuous learning and innovation within the team.
• Identify and mitigate risks associated with data architecture, including data security, privacy, and operational risks.
• Continuously monitor and optimize data architecture for performance, scalability, and cost-effectiveness.
• Ensure the architecture supports advanced analytics and reporting capabilities, enabling business intelligence and data-driven insights.

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