Data Management & Governance Program Lead

Data Management & Governance Program Lead

Role summary

Job title

Data Management & Governance Program Lead

Engagement

Enterprise data management programme for a life insurer: KPI standardization, data governance, data quality and lineage, platform assessment, growth analytics, AI readiness and BAU handover

Reports to

Engagement Director - Capvirge; works day to day with the client’s Deputy CTO

Leads

Project Manager, Project Coordinator, Data Governance Analyst, Data Quality & Lineage Analyst, Business Analyst, Power BI Developers and Snowflake Data Engineer (with the client EDO team)

Experience

12–15+ years in data and analytics; 5+ years leading data governance or data management programmes

Purpose of the role

The Program Lead designs, gets approved and hands over a complete data management framework, so the business trusts its numbers and can run it without the vendor after Go-Live.

The role owns nine strategic deliverables, from KPI definitions to BAU transition, and steers the client’s EDO team on day-to-day data operations and dashboard delivery. Success means one agreed meaning for every KPI, a named owner for every data asset, proven data quality, and a client team that runs it all on its own.

The role also oversees delivery of the 16 tactical dashboards (Power BI on Snowflake), from BRD and build to UAT sign-off, Production and BAU.

Key responsibilities

Strategy and frameworks

• Write and maintain the data management frameworks: KPI standardization, data governance, data quality and lineage, platform assessment, growth analytics, AI and automation readiness, handover, committee governance and BAU transition.
• Present frameworks and decision papers to the Steering Committee, the Data Management Committee and the Design Authority, and secure approval at each gate.

KPIs and data governance

• Run KPI definition workshops with each department; agree one definition, formula and owner per KPI; build the KPI Dictionary.
• Set up the governance operating model: Data Owners and Stewards, decision rights, policy, standards, data classification and committees.

Data quality, lineage and platform

• Identify critical data elements, define quality rules and thresholds, and set up monitoring, issue management and lineage.
• Assess the data platform (Snowflake, AWS, Power BI) for architecture, security, cost and environments; recommend a future state and remediation roadmap.

Analytics and AI readiness

• Map customer journeys and funnels; design reusable shared data sets for dashboards and reports.
• Assess AI readiness, rank automation and AI use cases, and set responsible AI and GenAI safety rules.

Delivery and operations

• Own the programme plan, RAID log, decision log, evidence repository and status reporting.
• Oversee the dashboard pipeline from BRD to UAT, Production and hypercare; unblock sign-offs with business owners.
• Guide the client EDO team on platform stability, incident and change processes, and the data request SOPs.

Handover and BAU

• Design BAU services, SOPs and service levels; train owners, stewards and the EDO team; lead handover acceptance and Go-Live readiness.

Team the role leads

Role

Focus

Project Manager

Programme plan, RAID log, action tracker and status reporting

Project Coordinator

Meetings, follow-ups, minutes and document control

Data Governance Analyst

Data Owners and Stewards, business glossary, policies and standards (D2)

Data Quality & Lineage Analyst

Data inventory, quality rules, monitoring and lineage (D3)

Business Analyst (insurance)

BRDs, KPI definitions and dashboard requirements (D1, D5)

Power BI Developers

Build, UAT and BAU support for the 16 dashboards

Snowflake Data Engineer

Data models, pipelines and platform remediation (D4)

Required experience and qualifications

• 12–15+ years in data and analytics, including 5+ years leading data governance or data management programmes end to end.
• 5+ years in insurance or financial services; life insurance strongly preferred.
• Has set up a data governance operating model (owners, stewards, decision rights, committees) and taken it through executive approval.
• Has standardized KPIs across business units and built a KPI or business glossary used in production reporting.
• Has delivered a data quality programme with critical data elements, rules, thresholds and lineage.
• Has led a platform or architecture assessment and a remediation roadmap on a modern cloud data stack.
• Has handed a programme over to a BAU team, with SOPs, training and acceptance.
• Experience as a consultant or vendor lead working with client executives.
• Bachelor’s degree in computer science, information systems, statistics, business or a related field; a master’s degree is a plus.

Technical and domain skills

Area

Must have

Good to have

Data governance

DAMA-DMBOK, federated operating models, RACI and decision rights, data classification

DCAM, data catalogue tools

KPIs and metrics

KPI trees, SMART definitions, certification and change control

Management scorecards and target setting

Data quality and lineage

Critical data elements, quality dimensions, rules and thresholds, issue management, column lineage

Automated quality checks in pipelines (Airflow), logical data models

Data platforms

Snowflake, AWS (S3), Power BI; environments, access control, cost (FinOps)

AWS Well-Architected, TOGAF, privileged access (CyberArk)

Analytics

Dashboard delivery lifecycle (BRD, UAT, hypercare), semantic models, shared data sets

Customer journey and funnel analytics

AI readiness

Use-case scoring; Predictive, Generative and Agentic AI concepts; responsible AI

NIST AI RMF, ISO/IEC 42001

Insurance domain

Life and ILP products, persistency, APE, claims, IFRS 17 and statutory reporting, bancassurance and digital partners

Group insurance

Regulation (Philippines)

Data Privacy Act (RA 10173), Insurance Commission requirements, RA 11765 consumer protection

AML (RA 9160)

Delivery and service

Programme planning, RAID, gates, change control; BAU service design, SOPs, incident and change processes

ITIL, ADKAR change management

Behavioural competencies

• Executive influence: gets decisions and sign-offs from senior leaders and busy business owners, and keeps approvals moving.
• Plain-language communication: turns frameworks into short material executives can read and act on.
• Facilitation: runs workshops where departments disagree, and brings them to one agreed definition.
• Pragmatism: balances good-practice governance with what a small team can actually run.
• Ownership: works without close supervision, and tracks commitments to closure.
• Coaching: builds capability in client teams so they can run the work after handover.

Preferred certifications

None is mandatory; proven delivery counts for more.

• CDMP (DAMA) or DCAM: data management
• PMP or PRINCE2: programme delivery
• SnowPro Core, or Microsoft Power BI Data Analyst: platforms
• ITIL 4 Foundation: BAU services
• An AI governance certification (for example IAPP AIGP): responsible AI

Work Location: In person