Enterprise Data Architect

We are seeking a highly experienced and strategic enterprise data architect to lead and shape the bank’s enterprise-wide data architecture and digital transformation initiatives. This leadership role is responsible for defining and implementing scalable, secure, and future-ready data strategies that support business growth, AI/ML innovation, regulatory compliance, and enhanced customer experiences.

Key responsibilities

1. Define and maintain the enterprise data architecture strategy and roadmap aligned with business objectives and digital transformation goals.
2. Design and govern scalable, secure, and high-performance data architectures across the full data lifecycle.
3. Establish and enforce data governance, quality, security, metadata management, and data lineage standards in compliance with banking regulations.
4. Lead modernization of enterprise data platforms and integrate cloud-native technologies, customer data platforms (CDP), and advanced analytics capabilities.
5. Architect and optimize data pipelines to support AI/ML model development, deployment, and advanced analytical initiatives.
6. Collaborate with senior leadership and cross-functional teams to drive data-centric decision-making and promote data literacy across the organization.
7. Mentor and guide data engineering and architecture teams while providing strategic technology leadership.

Applicant profile

1. Bachelor’s or master’s degree in computer science, data science, information systems, or a related field.
2. Minimum 10 years of experience in data architecture, including at least 5 years in an enterprise-level role within the financial services sector.
3. Strong expertise in enterprise data modelling, data governance frameworks, and data quality management.
4. Deep understanding of modern data architecture concepts including data lakes, data warehouses, data marts, data mesh, and data fabric.
5. Hands-on experience with cloud-based data platforms, especially Google Cloud technologies such as BigQuery, Dataflow, and Pub/Sub.
6. Strong knowledge of Hadoop ecosystem tools including HDFS, Hive, and Spark.
7. Experience with SQL, NoSQL, ETL pipelines, scripting languages (Python/Scala), data virtualization platforms, and enterprise ML platforms.
8. Proficiency in BI and reporting tools such as Tableau, Looker, or similar visualization platforms.
9. Familiarity with banking regulatory requirements and data security best practices.
10. Excellent communication, stakeholder management, and leadership skills with the ability to guide technical teams and present complex concepts to diverse audiences.