Field of studyBusiness Analytics, Computer Science, Data Engineering, Data Science, Information Systems, Information Technology, Statistics (or related)
Study levelBachelor's or higher
Original posting
Job Purpose:
Provide senior functional and operational leadership across Pakistan Single Window's Data Management Office, ensuring that enterprise data, business intelligence and analytics capabilities are delivered with strong governance, clear accountability, disciplined execution and sustainable technical foundations.
The role will translate organisational priorities into executable data initiatives, services and capabilities; strengthen delivery discipline; establish and enforce professional standards; and ensure that data governance, architecture, engineering, BI, analytics and data services operate as an integrated function.
A particular focus of the role will be to establish PSW's data reporting capability as a reliable, digital and governed service, progressively reducing dependence on manual reporting, field-based MIS processes and repeated domain-level data validation.
The initial priority of the role is to bring visibility, sequencing and delivery discipline to the Data Management Office's active initiatives and to operationalise governance across current data workstreams.
Within the first 30 days, the SVP is expected to complete a baseline assessment of active initiatives and present a prioritisation and delivery plan to the CEO and CEDO.
The SVP will work closely with the CEDO, executive management, business functions and technology teams, providing the CEDO with strong functional leadership, objective performance visibility and practical recommendations on delivery, capability, priorities and risks. Given the direct bearing of data delivery on enterprise-wide reporting and client commitments, portfolio-level delivery performance is reported directly to the CEO alongside functional reporting to the CEDO.
The role is expected to lead primarily through functional teams and professional leads, while remaining willing to engage directly on critical workstreams where doing so is necessary to establish delivery momentum, before transitioning fully to a leadership-through-others model as team capability matures.
Main Responsibilities:
Sections A–H define the full scope of the role. In the initial period, primary weight sits on B (Delivery, Performance and Reporting), C (Data Governance and Quality) and E (Data Reporting, BI, Analytics and Data Products); D, F and G are to be progressed in parallel; H operates as the ongoing stakeholder and engagement rhythm underpinning all of the above.
A. Data Function Roadmaps and Prioritization
Translate and improve the approved data strategy and priorities into executable functional roadmaps, delivery plans and initiatives.
Maintain functional roadmaps covering data governance, data platforms, reporting, BI, analytics and data services.
Ensure timely identification and escalation of delivery risks, bottlenecks and capacity constraints to the CEDO and, for portfolio-level delivery performance, to the CEO.
Challenge slippage, unclear ownership and weak delivery performance and drive corrective action through responsible teams.
Establish delivery metrics and dashboards providing transparent, decision-oriented visibility of portfolio performance.
Ensure delivery commitments are realistic, measurable and supported by appropriate technical and organizational capacity.
Work with the CEDO to resolve cross-functional issues requiring executive intervention.
Provide the CEO and CEDO with timely, accurate and decision-oriented reporting on portfolio delivery, functional performance, risks, dependencies, capability and resource requirements.
Establish mechanisms for measuring the effectiveness and maturity of data capabilities over time, and identify recurring delivery, governance or operational problems and drive their resolution.
Benchmark relevant data management, BI and analytics practices where useful, and ensure that lessons from major initiatives are captured and incorporated into future delivery.
C. Data Governance and Quality
Operationalize and enforce PSW's enterprise data governance framework established under the direction of the CEDO.
Establish practical mechanisms for data ownership, stewardship, classification, access, metadata, lineage, retention and accountability.
Define and maintain data quality standards, controls, measures and reporting mechanisms.
Establish processes for identifying, prioritizing and resolving material data quality issues.
Promote consistent business definitions, data standards and common terminology across PSW.
Ensure appropriate governance over critical and shared data assets.
Coordinate with Information Security, Legal, Risk, Internal Audit and relevant compliance functions to ensure that data access, classification, retention, privacy, sharing and usage comply with applicable legal, regulatory, contractual and organizational requirements.
Monitor adherence to data governance standards and escalate material gaps and risks.
Work with business and technology teams to embed governance into systems, processes and data products rather than treating it as a standalone compliance activity.
Implement agreed governance forums, decision rights and escalation mechanisms and ensure their effective operation.
D. Data Architecture and Engineering
Provide functional oversight of data architecture and engineering capabilities, ensuring that technical teams deliver against agreed architecture, standards, priorities and performance expectations.
Ensure that data architecture supports scalability, interoperability, reliability and future business requirements.
Oversee the development and evolution of appropriate data platforms, warehouses, lakes/lakehouses, marts and semantic layers.
Ensure effective use of ETL/ELT, APIs, streaming and other appropriate data integration technologies.
Establish and enforce professional standards for conceptual, logical and physical data models, enterprise semantic layers, common business definitions, reusable data structures, data pipelines, integration, metadata and data lifecycle management.
Review major architectural and platform decisions from a functional and business perspective and ensure alignment with enterprise architecture.
Ensure that technical debt, legacy dependencies and platform risks are identified and addressed through appropriate roadmaps.
Work with technology leadership and specialist teams to ensure that data platforms remain fit for purpose, secure and resilient.
E. Data Reporting, BI, Analytics and Data Products
Establish PSW's enterprise data reporting capability as a centralized, digital, governed and reliable service.
Develop a roadmap to progressively replace manual reporting and field-based MIS processes with automated data pipelines, standardized data definitions, validation controls, dashboards and self service reporting.
Identify and prioritize high-volume, repetitive and manually produced MIS reports for digitization, consolidation or elimination.
Establish standardized enterprise datasets, semantic models, KPIs and reporting definitions so that management information is generated from common governed data.
Establish mechanisms through which routine data validation and reconciliation are increasingly performed through automated controls rather than manual intervention by domain teams.
Ensure that data-quality exceptions are identified systematically and routed to the appropriate source or domain owner for resolution.
Establish service levels for reporting, including data refresh, availability, quality, incident resolution and report delivery.
Provide functional leadership for enterprise BI, reporting and analytics capabilities, ensuring executive and operational reporting is based on trusted, consistent and appropriately governed data.
Establish standards for dashboards, reporting, metrics, KPIs and analytical outputs, and promote governed self-service BI and analytics where appropriate.
Develop and oversee a roadmap for advanced analytics, predictive modelling, automation and AI enabled decision support, ensuring appropriate business validation, data quality, model governance, explainability, performance monitoring and responsible use.
Data services and products: lead the development of reusable data services, datasets, APIs, data products and analytical capabilities for PSW's internal and external requirements, with clear ownership, purpose, users, quality expectations, service levels and performance measures for material data services.
Develop and maintain roadmaps for strategic data products and services, and promote reuse of common data assets and services across PSW rather than development of isolated solutions.
F. Functional Leadership and Professional Capability
Monitor adoption, performance, quality and business value of material data products and services, and work with business and technology stakeholders to identify opportunities for new data-enabled services.
Reduce duplication of reporting and promote reusable analytical assets and common data models.
Provide senior professional leadership across the data, BI and analytics disciplines within the Data Management Office.
Establish clear professional expectations, standards, ways of working and accountability mechanisms across the function.
Assess the capability, capacity and skills required to deliver the data portfolio effectively and provide objective recommendations to the CEDO.
Identify capability gaps and recommend appropriate responses, including recruitment, development, restructuring, outsourcing or specialist support where appropriate.
Develop and strengthen functional leads and managers to create leadership depth within the data organization.
Establish mechanisms for knowledge sharing, professional development and cross-functional collaboration.
Provide the CEDO with objective assessments of functional performance, capability constraints and material people-related risks.
Support the CEDO in resolving organizational bottlenecks that affect data delivery.
Identify succession and continuity risks within critical technical and functional areas and recommend mitigation measures.
G. Stakeholder and Business Engagement
Develop effective working relationships with business functions, technology teams and other key stakeholders to ensure that data capabilities respond to actual business requirements.
Translate business requirements and organizational priorities into practical data, reporting and analytics solutions.
Ensure that data teams understand the operational context and business purpose behind their work.
Facilitate resolution of conflicting data requirements, priorities and definitions across business functions.
Communicate complex data and analytical matters clearly to non-technical stakeholders.
Establish mechanisms for capturing stakeholder feedback and using it to improve data services and delivery.
Promote practical adoption of data, BI and analytics in business decision-making.
H. Portfolio, Resource and Vendor Management
Maintain visibility of the Data Management Office's portfolio, resource requirements, dependencies and delivery capacity.
Support prioritization and allocation of resources across competing initiatives.
Monitor utilization and identify material under-capacity, over-capacity or capability constraints.
Ensure that external vendors and partners supporting data initiatives have clear deliverables, responsibilities, performance expectations and governance arrangements.
Monitor vendor performance and escalate material commercial, delivery or technical risks.
Requirements
Minimum Qualifications:
Bachelor's or Master's degree in Computer Science, Information Technology, Information Systems, Data Science, Data Engineering, Statistics, Business Analytics or a related discipline.
Minimum 16 years of education.
Professional Experience:
Minimum 14 years of relevant professional experience, including substantial experience in enterprise data, business intelligence, analytics or related technology functions.
Demonstrated experience in a senior leadership role managing complex data or digital portfolios.
Proven record of improving delivery performance and establishing management discipline in complex technology environments.
Demonstrated experience in stabilizing and strengthening delivery performance in complex or transformation-focused technology environments, including establishing clear prioritization, sequencing, ownership, accountability and execution discipline
Experience leading multidisciplinary teams and working across business and technology functions.
Demonstrated experience implementing or operating enterprise data governance and data quality frameworks.
Strong understanding of data architecture, data integration, ETL/ELT, APIs, data platforms and modern data management practices.
Experience with enterprise BI and analytics platforms such as Power BI, Tableau or Oracle Analytics.
Working understanding of SQL and relevant analytical/programming environments such as Python or R.
Understanding of AI/ML, advanced analytics and responsible use of AI in enterprise environments.
Strong understanding of data security, privacy, access control and information lifecycle considerations.
Experience in development or management of data services, APIs, analytical products or reusable enterprise data assets.
Experience in large-scale technology, digital transformation, financial services, public-sector or trade related environments would be an advantage.
Leadership Experience:
The successful candidate should demonstrate:
Experience managing senior technical and professional staff.
Ability to establish accountability and delivery discipline without becoming unnecessarily operational.