As a Lead Data Architect, within the Corporate Technology team, you will establish, develop, and maintain standards for organizing digital content and data that support usability and operational efficiency, and enable consistent adoption across platform and develop conceptual, logical, and physical data models aligned with business objectives, ensuring traceability from business concepts to implementation.

Job Responsabilities:

• Collaborate with and influence CDO, Engineering, and business partners to align information flows and canonical models for interoperability and reuse.
• Impact the creation of high-quality Data Products aligned with the Corporate Technology (CT) Data Mesh Strategy and ensure compliance with Corporate Technology OKRs.
• Represent the tower in Corporate Technology Information Architecture forums; contribute patterns, reference models, and guidance for broader adoption and actively participate in architectural and governance forums to address challenges, resolve design issues, and evolve standards/practices. and mentor and support architects and developers in data-related skills (modeling, lineage, quality, metadata, standards) to uplift craftsmanship. and design, develop, and implement data governance processes and solutions to ensure compliance with Firmwide/LOB Data Risk Management Policy and Standards.
• Identify and implement automation opportunities for data controls, reducing manual effort and strengthening preventative and detective measures and participate in CT Data Governance Working Groups to resolve issues and evolve governance standards/practices. and collaborate with the Chief Data Office to interpret and operationalize governance requirements for corporate functions and application owners (AOs).
• Monitor and socialize Data Risk Metrics (e.g., FDRM metrics) with senior stakeholders; partner with application teams to remediate control gaps and facilitate communication and change management with application teams to ensure understanding of governance obligations and delivery timelines.
• Develop and promote best practices in data tiering, lineage, quality, retention, destruction, and localization controls; embed them in design reviews.
• Advise cross-functional teams on data architecture solutions that satisfy governance and regulatory requirements without sacrificing scalability or cost efficiency. and support audit, cyber, and control stakeholders during reviews of Corporate Technology data governance and data risk controls; coordinate evidence and follow-ups.

Required qualifications, capabilities, and skills:

• 7+ years in data architecture, information architecture, or enterprise data design roles, with hands-on experience defining standards and data models.
• Demonstrated experience designing conceptual/logical/physical data models and canonical information architectures; proficiency with one or more modeling tools (e.g., ER/Studio, ERwin, Archi Mate, UML).
• Proven track record implementing data governance processes at scale (policies, standards, controls, metrics, and evidence).
• Practical knowledge of metadata management, lineage, data quality frameworks, and cataloging solutions.
• Experience collaborating with CDO and technology stakeholders and influencing without direct authority in a matrixed environment.
• Strong understanding of data risk management, regulatory drivers, and controls (e.g., data classification/tiering, retention, localization).
• Excellent communication skills with the ability to translate business requirements into data designs and governance solutions.

Preferred qualifications, capabilities, and skills:

• Familiarity with Data Mesh principles and data product thinking; experience aligning architecture and controls to mesh-aligned operating models.
• Experience with cloud data platforms and lakehouse technologies (e.g., AWS/GCP, Snowflake/Databricks, Delta/Iceberg), and how governance/controls are implemented in these environments.
• Hands-on exposure to lineage and data quality tooling and automation (e.g., Open Lineage, Great Expectations, Soda, Monte Carlo, Collibra/Alation).
• Understanding of Fin Ops and cost-aware data design decisions (storage classes, partitioning, lifecycle policies).
• Prior participation in audit, cyber, and control reviews; experience organizing evidence and remediation plans.