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Lead enterprise data architecture strategy and governance as senior vice president at Citi.
Drive data-driven decisions across finance, risk, and tech teams in the banking sector.
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Job Overview
Role Overview
The Data Architecture Lead is a strategic professional who closely tracks emerging trends in data architecture, data virtualization, data mesh, data engineering, analytics, and governance, and applies them within the organization. This role represents one of the highest levels of subject matter expertise in data architecture within the business.
Strong communication and influencing skills are essential to drive alignment across senior stakeholders, business leaders, and technology teams. The individual is accountable for delivering enterprise data architecture strategy, governance frameworks, and data platform evolution, with direct impact on business outcomes and data-driven decision-making.
The role requires ownership of data strategy, architecture standards, and execution across a sub-function or domain, along with leadership responsibilities for team management, resource allocation, and delivery oversight.
Key Responsibilities
Data Architecture & Strategy
Define and drive the enterprise data architecture vision, ensuring alignment with business strategy and enterprise architecture principles.
Architect and implement data virtualization solutions to unify access to distributed data sources across Finance, Risk, and enterprise systems.
Define integration patterns for seamless data consumption across platforms, ensuring performance, scalability, and security.
Establish and maintain data models (conceptual, logical, physical) across domains to enable consistency and reuse.
Develop long-term data strategy, roadmap, and target-state architecture, including modernization initiatives (cloud, real-time, AI/ML enablement).
Enable business domains to operate as data product owners, ensuring clear ownership, discoverability, and usability of data assets.
Establish standards for data product design, SLAs, interoperability, and lifecycle management.
Drive adoption of federated governance while maintaining alignment with enterprise controls and regulatory requirements.
Data Governance & Quality
Define and implement data governance frameworks, including metadata management, lineage, cataloging, and data quality standards.
Ensure compliance with regulatory requirements, data privacy, and security policies.
Partner with risk, compliance, and control functions to mitigate data-related risks and ensure audit readiness.
Act as a senior advisor on data integrity, reconciliation, and control frameworks.
Technology & Platform Leadership
Provide architecture leadership for data platforms, integration patterns, APIs, and data pipelines (batch and real-time).
Evaluate emerging technologies and guide adoption of cloud data platforms such as Data Bricks, big data technologies, and analytics tools.
Collaborate with engineering teams to establish best practices for data ingestion, transformation (ETL/ELT), and consumption.
Stakeholder Engagement & Leadership
Act as a trusted advisor and thought leader for business and technology stakeholders on data-related initiatives.
Partner with cross-functional teams (product, engineering, analytics, business) to shape data-driven solutions.
Influence senior leadership on data investment priorities and strategic decisions.
Delivery & Execution Oversight
Provide architectural oversight for end-to-end data initiatives, including planning, prioritization, and execution.
Contribute to budgeting, vendor selection, and technology roadmap planning.
Ensure successful delivery of projects aligned with architectural standards and business objectives.
Risk & Compliance
Proactively assess risks in data architecture decisions, ensuring protection of organizational data assets.
Drive adherence to policies, standards, and ethical use of data.
Escalate and manage control issues with transparency.
Leadership & Team Management
Lead and mentor a team of data architects and engineers.
Manage resource allocation, workload prioritization, and capability development within the team.
Foster a culture of innovation, accountability, and continuous learning.
Qualifications
- 12+ years of experience in data architecture, data engineering, or related fields.
- Proven experience in: Designing and implementing enterprise-scale data platforms and architecture. Designing and scaling data mesh and Virtualization architecture. Exposure in integration with enterprise data lineage, catalog, and data quality tools
- Strong understanding of: Data modeling techniques (dimensional, normalized, data vault) Data governance and data quality frameworks Distributed data systems and cloud data platforms
- Experience with modern technologies such as: Big data frameworks (Spark, Hadoop) Data warehousing/lakehouse solutions (Snowflake, Databricks)
- Excellent communication, stakeholder management, and influencing skills.
- Ability to manage multiple priorities in a fast-paced environment.
- Strong analytical and problem-solving capabilities with attention to detail.
- Self-driven with the ability to lead strategic initiatives independently.
Nice-to-Have
- Experience in financial services or regulated industries.
- Exposure to AI/ML data pipelines and advanced analytics architectures.
Education
- Bachelor’s/University degree in Computer Science, Engineering, or related field.
- Advanced degree (Master’s) preferred.
Job Family Group:
Technology
Job Family:
Applications Development
Time Type:
Full time
Most Relevant Skills
Please see the requirements listed above.
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.
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