ICT Specialist - Data Engineering (Open to Tier 1 and 2 candidates only)

Tiered Approach In line with the commitment to safeguard capacity and support personnel already in the Organization, a majority of UNDP UNCDF/UNV vacancies are advertised using a tiered application process whereby: Tier 0: UNDP/UNCDF/UNV IP staff holding permanent (PA) and fixed-term (FTA) appointments, whose posts will be abolished, or contracts will be terminated or not renewed during 2026. Tier 1: Other UNDP/UNCDF/UNV staff holding permanent (PA) and fixed-term (FTA) appointments Tier 2: UNDP/UNCDF/UNV staff holding temporary appointments (TA), personnel on regular PSA contracts, and Expert and Specialist UN Volunteers Tier 3 or no tier indicated: All other contract types from UNDP/UNCDF/UNV and other agencies, and other external candidates Please make note of the Tier(s) indicated in the vacancy title, if any, and ensure that you satisfy the eligibility to apply. Background The Information and Communication Technology Section (ICTS) serves as a strategic enabler and innovation partner for the United Nations Volunteers (UNV), supporting digital transformation across the organization. ICTS provides and evolves cloud platforms, business applications, enterprise data services, cybersecurity capabilities, service management, automation, AI-enabled solutions, and a modern digital workspace for UNV. The ICTS Data Team enables evidence-based decision-making by developing and maintaining enterprise data pipelines, data models, analytics products, governance practices, and business intelligence solutions. The role provides seasoned technical and advisory support to strengthen UNV's enterprise data platform, ensuring that data from volunteer management, HR, finance, programme, ERP, and external sources is integrated, governed, documented, monitored, and made available for reliable reporting, analytics, and emerging AI/ML use cases. Scope of Work The ICT Specialist - Data Engineering will provide seasoned technical leadership, subject matter expertise, platform modernization, data governance, DataOps, and analytics enablement support to UNV ICTS. The role functions as a lead technical resource for enterprise data engineering and related platform capabilities, providing technical authority through architecture guidance, engineering standards, quality assurance, and expert advisory services rather than through direct supervision of UNV personnel. Duties and Responsibilities A. Technical leadership, subject matter expertise, and enterprise data platform architecture Act as a technical authority for UNV ICTS Data Teams's enterprise data engineering function, providing expert guidance on enterprise data architecture, platform design, data integration strategies, governance frameworks, and engineering standards. Serve as UNV ICTS Data Team's technical subject matter expert for enterprise data engineering and data platform architecture, providing authoritative technical guidance for the design, evolution, modernization, and optimization of UNV's enterprise data platform, including cloud-native data pipelines, data stores, transformation frameworks, governance capabilities, and analytics enablement. Exercise independent technical judgment, establish technical best practices, influence technology direction, and provide authoritative advice to management, stakeholders, and external service providers on matters relating to enterprise data platforms, analytics capabilities, and data governance. Establish and maintain technical standards, architectural principles, reusable engineering patterns, and best practices to support organizational reporting, analytics, AI-enabled solutions, and data-driven decision-making. B. Data integration, transformation, and engineering delivery Design, develop, and optimize scalable ETL/ELT workflows to ingest, cleanse, transform, and load structured and semi-structured data from UNV source systems and external APIs into governed data stores. Develop reusable transformation frameworks using Azure Data Factory, Databricks, PySpark/Python, SQL, Delta Lake, and related tools to ensure repeatable, idempotent, version-controlled, and performant data processing. Implement reliable data-processing logic for batch and, where applicable, near-real-time data flows, ensuring that pipelines are resilient to source-system changes and operational interruptions. C. Data modelling, semantic layer, and analytics enablement Lead the design and enhancement of dimensional, normalized, and semantic data models that support operational reporting, self-service analytics, enterprise dashboards, and advanced analytical workloads. Collaborate with business analysts, BI developers, and stakeholders to translate business requirements into trusted data products that are usable, performant, and aligned with UNV reporting needs. Support optimization of Power BI semantic models, DAX measures, data refresh processes, and model performance in collaboration with BI colleagues. D. Data quality, governance, lineage, and compliance Design and implement data validation frameworks, quality checks, anomaly detection, lineage documentation, technical metadata repositories, and catalog entries to improve data reliability and transparency. Advise on practical implementation of data governance principles, access patterns, documentation standards, and compliance-oriented controls in data engineering solutions. Document business rules, data definitions, transformation logic, dependencies, known limitations, and data-quality thresholds for critical datasets. E. DataOps, automation, CI/CD, and observability Develop and maintain CI/CD pipelines for data engineering code using Azure DevOps, GitHub Actions, or similar tooling. Implement automated testing, validation, and deployment practices across development, staging, and production environments. Establish monitoring, alerting, and logging using appropriate observability tools to support reliable operations, incident analysis, and continuous improvement. F. Business intelligence, reporting, and decision support Support the design, development, optimization, and lifecycle management of Power BI reports, dashboards, data models, measures, and analytical products. Ensure BI solutions are grounded in reliable, well-governed data and are documented through functional specifications, business rules, data model diagrams, measures, deployment pipelines, and UAT criteria. Contribute to improved data accessibility and usability for management reporting, operational monitoring, and evidence-based decision-making. G. Stakeholder engagement, knowledge sharing, and continuous improvement Work with ICTS teams, business sections, data consumers, and stakeholders to identify data needs, define data-product requirements, resolve data-quality issues, and promote a data-driven culture. Produce and maintain pipeline architecture diagrams, data dictionaries, runbooks, deployment guides, and knowledge materials. Participate in code reviews, peer testing, engineering best-practice discussions, and continuous improvement activities. Perform other duties within the functional profile as deemed necessary for the efficient functioning of the Office and the Organization. Institutional Arrangement The NPSA holder will work under the direct supervision of the Team Lead of the UNV ICTS Data Team. The incumbent will serve as a lead technical resource and subject matter expert for enterprise data engineering and related platform capabilities within UNV. The role provides technical leadership through architecture guidance, engineering standards, quality assurance, and expert advisory services. While the position has no formal supervisory responsibility over UNV personnel, the incumbent will be expected to coordinate, guide, review, and manage the work of external vendor consultants, contractors, and technical resources engaged by UNV for data engineering, analytics, reporting, and related digital initiatives. The incumbent will provide technical direction, prioritize deliverables, review outputs, ensure quality standards, and support acceptance of vendor-delivered solutions. The role is home-based and will work collaboratively with ICTS colleagues, data engineers, BI developers, business analysts, and business stakeholders using agreed ICTS work-management, version-control, sprint, documentation, and deployment practices.

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