Head Data Management & Governance

THE COMPANY

Singlife's purpose is to democratize access to meaningful financial protection. Our unique business model uses the smartest technologies to put you in control of your finances, by providing tools and solutions that fit your needs, your budget, and are always on hand. A start-up in a long-standing industry, the company has showed how combining new technologies with life insurance makes it possible for people to manage their finances more effectively and efficiently. Singlife is growing at an exceptional rate driven by a growing portfolio of partnerships with platforms like GCash, where we make our meaningful insurance propositions more accessible to the wider market.

Job Summary:

The Senior Manager Data Management & Governance is accountable for establishing, operating, and continuously improving the organization's enterprise data governance, data management, and data quality framework.

This role ensures that data is trusted, secure, compliant, discoverable, and fit-for-purpose across operational systems, analytics platforms, and digital channels. It is the executive owner of data policies, standards, data ownership, data quality management, metadata, lineage, and regulatory data controls.

The role partners closely with Business, IT, Risk, Compliance, Architecture, and Analytics teams to institutionalize data as a strategic enterprise asset while ensuring compliance with data privacy laws, regulatory requirements, and internal control frameworks.

Key Responsibilities:

Data Governance Framework & Operating Model

Define, implement, and operate the enterprise data governance framework including:

• Data ownership and stewardship model
• Data domains and critical data elements (CDEs)
• Data policies, standards, and procedures

Establish and run the Data Governance Council and domain working groups.

Ensure governance is embedded into delivery, operations, and change processes

Data Management & Standards

Own and enforce standards for:

• Master data
• Reference data
• Transactional data
• Analytical data

Define:

• Data lifecycle management
• Data classification and handling
• Retention and archiving policies

Ensure consistent data definitions across systems and platforms

Data Quality Management

Define and implement:

• Enterprise data quality framework
• Data quality rules, thresholds, and controls
• Data quality monitoring and dashboards

Own:

• Data issue management process
• Root cause analysis and remediation tracking

Ensure data quality is proactively measured and continuously improved.

Metadata, Lineage & Data Catalog

Own enterprise:

• Business glossary
• Technical metadata
• Data lineage
• Data catalog capabilities

Ensure:

• Traceability from source to consumption
• Transparency for regulatory and audit requirements

Promote self-service data discovery and understanding.

Regulatory, Privacy & Compliance Data Controls

Ensure compliance with:

• Data Privacy Act / GDPR
• Insurance / banking regulatory requirements
• Internal audit and risk policies

Define and enforce:

• Data access controls
• Data masking and anonymization
• Consent and purpose limitation controls

Act as data governance lead during audits and regulatory reviews.

Data Architecture & Platform Alignment

Partner with:

• Enterprise Architecture
• Data Platform teams
• Application teams

Ensure:

• Governance is embedded into data pipelines, DWH, lakehouse, and integration layers
• New systems and platforms comply with data standards by design

Enforce governance-by-design, not governance-after-the-fact.

Operating Model, Adoption & Change

Build and lead:

• Data governance office / team
• Data stewards community

Drive:

• Training and awareness
• Cultural adoption of data ownership

Embed data governance into:

• SDLC
• Change management
• Procurement and vendor onboarding

Metrics, Reporting & Continuous Improvement

Define and report:

• Data quality KPIs
• Governance adoption metrics
• Compliance metrics

Produce executive dashboards on data health and risk posture.

Drive a continuous improvement roadmap for data management maturity.

Qualifications:

Bachelor's degree in information systems, Computer Science, Data, or related field.

10+ years experience in:

• Data management
• Data governance
• Data quality
• Analytics or enterprise systems

5+ years in senior or enterprise leadership roles in data or technology.

Strong experience in:

• Data governance frameworks (DAMA, DCAM, etc.)
• Data quality management
• Metadata and lineage
• Regulatory data management

Experience in regulated industries (insurance, banking, financial services).

Preferred Qualifications:

Master's degree in Data, IT, or Business

DAMA-DMBOK, DCAM, or similar certifications

Experience with:

• Data catalogs
• Data quality tools
• Master data management platforms

Experience with:

• Cloud data platforms
• Modern data stack (DWH, lakehouse, streaming, BI)

Leadership Competencies:

• Strong governance mindset balanced with pragmatism
• Influential, consensus-building leadership style
• Highly structured, policy- and control-oriented
• Strong business and regulatory communication skills
• Change leadership and cultural transformation capability
• Executive-level stakeholder management