Data Governance

About the role

East West Bank is seeking an experienced Data Governance professional to help develop, operationalize, and mature the bank's enterprise data governance program. This role will drive development of the enterprise data governance program, with accountability for Critical Data Elements (CDEs), data quality, metadata management, data lineage, regulatory compliance, and governance controls supporting enterprise reporting, risk management, analytics, and AI initiatives.

Responsibilities

• Support the implementation and ongoing maturation of the enterprise data governance framework, including policies, standards, procedures, and operating models.
• Partner with business and technology stakeholders to identify and document Critical Data Elements (CDEs), data ownership and stewardship assignments, business glossaries, data definitions, lineage, and data flows.
• Facilitate governance working sessions with business and technology teams to align standards, remediation priorities, and governance objectives.
• Develop and maintain enterprise data dictionary, data cataloging, and lineage documentation using tools such as Microsoft Purview, Collibra, Alation, or Informatica.
• Drive selection and adoption of modern data governance tools as needed.
• Aid in enterprise data quality management processes, including quality rules, controls, issue remediation, root-cause analysis, scorecards, and KPI reporting.
• Collaborate with data engineering, analytics, and architecture teams to embed governance controls within enterprise data pipelines and analytical platforms.
• Support governance activities related to data classification, sensitive data handling, access governance, retention, lifecycle management, and audit requirements.
• Prepare governance reporting materials and metrics for governance councils, leadership reviews, and regulatory or audit activities.
• Promote data literacy and governance best practices across the organization.
• Support governance enablement within Azure-based environments including Azure Data Lake, Azure Databricks, Microsoft Fabric, and Power BI ecosystems.
• Perform other duties as assigned.

Qualifications

• Bachelor’s degree in Information Systems, Data Management, Computer Science, Business Analytics, or a related discipline.
• 8+ years of experience in data governance, AI governance, metadata management, data quality, data risk, or related enterprise data roles.
• Strong understanding of data governance concepts including stewardship, metadata management, lineage, data quality, and business glossaries.
• Experience supporting enterprise governance programs using tools such as Microsoft Purview, Collibra, Alation, or Informatica.
• Familiarity with modern cloud-based data ecosystems, particularly Azure-centric environments.
• Hands-on SQL skills to analyze data quality issues, metadata structures and governance controls implemented.
• Understanding of banking regulatory and compliance considerations related to data governance and risk management.
• Strong analytical, organizational, communication, and stakeholder management skills.
• Experience leading cross-functional governance initiatives with measurable outcomes and executive visibility.
• Ability to translate complex AI and data risks into practical business and control decisions.

Required AI & Data Governance Experience

• Hands-on experience applying governance controls to AI-enabled solutions, including data quality, lineage, ownership, access controls, retention, consent, and auditability.
• Experience governing AI use cases from intake through production, including risk assessment, approval workflows, monitoring, and issue remediation.
• Practical experience governing data used in LLM and RAG solutions, including source validation, sensitive data handling, ingestion controls, metadata management, and knowledge-base quality.
• Strong understanding of AI risk, model risk, data privacy, explainability, human oversight, and responsible AI practices in regulated environments.
• Ability to translate governance policies into repeatable operational procedures, controls, evidence requirements, metrics, and executive reporting.

AI Fluency & Hands-On LLM Experience

• Practical experience with major LLM platforms including OpenAI, Anthropic Claude, Microsoft Copilot/Azure OpenAI, Google Gemini, AWS Bedrock, and open-source models such as Llama or Mistral.
• Familiarity with prompt engineering, embeddings, vector search, RAG, agentic workflows, model evaluation, hallucination mitigation, and human-in-the-loop review.
• Ability to assess AI solutions for privacy exposure, explainability, monitoring requirements, production readiness, and governance risks.
• Experience partnering with technology, risk, compliance, legal, and business teams to govern AI responsibly while enabling innovation.

Required Technologies & Tooling

• Data governance and catalog platforms such as Microsoft Purview, Collibra, Alation, or Informatica.
• Cloud data platforms and lakehouse architectures including Snowflake, Databricks, Azure, AWS, or GCP.
• Data pipeline and orchestration tools such as dbt, Airflow, Azure Data Factory, AWS Glue, and Kafka.
• AI/ML lifecycle and monitoring tools including MLflow, model registries, evaluation frameworks, observability tools, and LLM monitoring platforms.
• Security controls including IAM, encryption, DLP, tokenization/masking, secrets management, and audit logging.
• Familiar with Vector databases and AI infrastructure platforms such as Pinecone, Weaviate, FAISS, pgvector, and Azure AI Search.