Data Domain Governance Lead

Title: Data Domain Governance Lead

Location: Bromley, UK

Job Type: Hybrid(3Days/Onsite)

• The Data Domain Governance Lead is responsible for embedding robust data governance practices across Agile delivery teams within banking domain. Operating within a SAFe Agile framework, this role ensures that data standards, models, lineage, privacy, and controls are consistently applied across delivery pipelines. The successful candidate will bring deep expertise in data architecture, metadata management, privacy compliance, and data quality, enabling scalable and audit-ready data ecosystems.

Key Responsibilities:

Portfolio & Program Alignment

Ensure governance guardrails are considered during PI Planning and reflected in Agile Features.

Act as a System Architect and Shared Services SME to support Agile Release Trains (ARTs) in embedding governance principles.

Provide expert guidance to Agile Teams on integrating data governance into delivery workflows and feature design.

Canonical Data Models & Standards

Collaborate with Business and Source of Record (SOR) stakeholders to define and maintain domain-level canonical (conceptual) data models.

Review metadata/catalogue entries to ensure critical datasets have clear ownership, lineage, and business semantics.

Support Agile Teams in applying consistent data modelling practices and semantic layer alignment.

Data Quality & Controls

Translate EDM policies into domain-specific rules and acceptance criteria.

Partner with delivery teams to embed automated data quality checks and validation rules into data pipelines.

Ensure Features include required data quality controls; contribute to Definition of Done and acceptance criteria.

Metadata, Lineage & Privacy Governance

Guide Agile Teams in embedding lineage capture and metadata tagging within delivery pipelines using modern tooling (e.g., Collibra, Informatica, Alation).

Ensure consistent application of metadata standards and lineage tracking across domains.

Oversee PII and sensitive data classification, labelling, and handling in accordance with data privacy regulations.

Define and enforce archival and retention policies for domain data assets, ensuring compliance and operational efficiency.

Audit Insights & Continuous Improvement

Analyse audit findings and feedback to identify gaps and improvement opportunities in data governance practices.

Strengthen domain-level data controls and quality frameworks based on audit insights.

Required Skills & Experience:

Strong understanding of Treasury and/or Corporate Banking domains, including products, processes, and regulatory requirements.

• Advanced data modelling expertise, including:
  • Designing conceptual, logical, and physical models for complex financial domains.
  • Applying semantic modelling for BI and analytics.
  • Knowledge of normalization, denormalization, and performance optimization.
• Hands-on experience with modelling tools such as ERwin, PowerDesigner, or equivalent.
• Strong background in enterprise data architecture.
• Hands-on experience with metadata and lineage tools such as Collibra, Informatica Enterprise Data Catalog, Alation, Microsoft Purview

• Deep understanding of data privacy regulations and compliance standards and local banking regulations.
• Experience implementing PII classification, data sensitivity labelling, and access controls using tools
• Knowledge of data archival and retention policies relevant to financial records and audit trails.
• Experience defining and implementing automated data quality rules and controls for datasets.

Preferred Qualifications:

Experience implementing industry-standard data governance frameworks such as DAMA-DMBOK, DCAM, and COBIT.

Experience in large-scale financial institutions or treasury functions.

Knowledge of data vault modelling and graph modelling for advanced use cases.

Familiarity with data observability tools and practices to monitor data health, lineage, and reliability across pipelines