Data Management & Governance Program Lead

Data Management & Governance Program Lead

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

Location: Manila, Philippines (hybrid) – to confirm

Reports to: Engagement Director (vendor); works day to day with the client’s Deputy CTO

Leads: Project Manager, Project Coordinator and client EDO technical leads (BI, Snowflake, Power BI)

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.

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.

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.

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

Success in the first 6 months

By

Measure

Month 1

Data Owners and Stewards named; access in place; plan, RAID and decision log running

Month 2

KPI, governance and data quality frameworks approved by the committees

Month 3

Platform roadmap approved; monthly data quality score live

Month 4

Growth blueprint and AI readiness plan approved

Month 5

Committee charter approved; handover items accepted by named owners

Month 6

Go-Live: BAU team runs the services without vendor support; Enterprise KPIs certified where a source exists

Work Location: In person