Data Governance Lead – Payment Transaction Banking

Data Governance Lead Payment / Transaction Banking

Visa: any but no OPT CPT

Location: New York Onsite job ..Final interview onsite too

Role Summary

The Data Governance Lead is responsible for embedding robust scalable data governance practices across Payment and Transaction Banking platforms. The role works closely with delivery architecture and business teams to ensure data standards models lineage quality privacy and controls are consistently applied across modern payment ecosystems. This position plays a critical role in enabling regulatory-ready audit-ready and analytics-ready data foundations within large-scale agile delivery environments.

Key Responsibilities

1. Data Governance Strategy & Agile Enablement

• Embed data governance guardrails into agile delivery lifecycles including feature design acceptance criteria and release planning.
• Act as a domain data governance SME supporting multiple delivery teams working on Payment platforms and data products.
• Translate enterprise data governance policies into actionable domain-specific implementation guidance for delivery teams.

2. Canonical Data Models & Payment Data Standards

• Define and maintain domain-level canonical and conceptual data models for Payment and Transaction Banking.
• Ensure consistent application of Payment data standards across platforms covering clearing settlement liquidity reconciliation and reporting.
• Support semantic consistency aligned to ISO 20022 and modern Payment data models.

3. Metadata Lineage & Data Transparency

• Drive consistent metadata capture ownership definition and end-to-end lineage across Payment data pipelines.
• Ensure critical datasets have clear business definitions ownership and traceability from source systems to downstream consumption.
• Partner with technology teams to embed lineage and metadata tagging within automated delivery pipelines.

4. Data Quality Controls & Regulatory Readiness

• Define domain-specific data quality rules and controls aligned to regulatory risk and operational requirements.
• Ensure automated data quality checks are embedded into Payment data pipelines and platforms.
• Contribute to Definition of Done and acceptance criteria to ensure data quality and control requirements are met before release.

5. Privacy Sensitivity & Data Lifecycle Management

• Oversee classification labelling and handling of sensitive and customer data within Payment datasets.
• Ensure compliance with data privacy regulations and internal control standards.
• Define and enforce data archival and retention policies for Payment data supporting audit regulatory and operational needs.

6. Audit Insights & Continuous Improvement

• Analyze audit findings and control feedback to identify governance gaps and improvement opportunities.

Required / Must-have Skills

• Strong domain experience in Payment / Transaction Banking including exposure to payment rails such as real-time Payment ACH SWIFT clearing and settlement.
• Hands-on experience defining and governing data across large-scale Payment platforms or data ecosystems.
• Advanced data modelling expertise (conceptual logical and physical) including semantic modelling for analytics and reporting.
• Strong understanding of enterprise data architecture and data platform design.
• Practical experience with metadata management data cataloguing and lineage capabilities.
• Proven experience implementing automated data quality rules controls and validation frameworks.
• Solid understanding of data privacy sensitive data handling and regulatory expectations in banking environments.
• Experience working in large complex regulatory-aware organisations using agile or scaled-agile delivery models.

Preferred / Nice-to-Have Skills

• Exposure to industry data governance frameworks (e.g. DAMA-DMBOK DCAM COBIT).
• Experience within large global banks Payment transformation programs or Transaction Banking functions.
• Familiarity with advanced data modelling techniques such as data vault or graph-based models.
• Knowledge of data observability and monitoring practices to assess data health and reliability across pipelines.