Mid-Level Data Integration Engineer

For 70 years, UNICEF has been working on the ground in 190 countries and territories to promote children's survival, protection and development. The world's largest provider of vaccines for developing countries, UNICEF supports child health and nutrition, good water and sanitation, quality basic education for all boys and girls, and the protection of children from violence and exploitation. UNICEF believes that all children have a right to survive, thrive and fulfil their potential – to the benefit of a better world. UNICEF is funded entirely by the voluntary contributions of individuals, businesses, foundations and governments.

With over 150 country offices, UNICEF relies on a consolidated KPI dashboard to monitor performance across global operations. However, this system was built over a decade ago using SQL Server Reporting Services (SSRS). While it has provided a vital baseline for field monitoring, the legacy technology and underlying business logic have become outdated, limiting the organization’s ability to leverage modern, real-time data insights.

To address these gaps, UNICEF is transitioning to a more agile reporting ecosystem. This role will focus on modernizing the dashboarding tools and refining the business logic to ensure country offices have the intuitive, high-performance analytics needed for contemporary decision-making. By moving away from static SSRS reports, you will help transform how the organization tracks and scales its impact worldwide.

Under the direct supervision of Business Intelligence Specialist, the Mid-Level Data Integration Engineer will:

· Pipeline & Solution Engineering: Translate business requirements into cost-efficient, automated data pipelines to achieve high-value analytics outcomes.

· Data Migration & Modernization: Modernize the existing on-premises data platform to a cloud native platform (Databricks).

· Data Warehouse Management & Performance: Monitor and performance tune pipelines in the on-premises Oracle Data Warehouse while coordinating planned weekly releases.

· Observability & Quality Assurance: Implement monitoring and testing protocols to ensure high data quality, health, and reliability across daily warehouse refreshes.

· Data Modelling & Architecture: Design, validate, and maintain logical data models, metadata standards, and data architecture frameworks aligned with business needs.

· CI/CD & Release Management: Manage continuous integration, automated testing, and release deployments to safely introduce schema and pipeline changes into production.

· Troubleshooting & Stakeholder Communication: Promptly diagnose and resolve production data issues, documenting technical/business specs and maintaining active alignment with stakeholders.

· Technical Leadership & Collaboration: Foster cross-team data sharing, enforce engineering standards, present recommendations to diverse audiences, and guide external vendors and consultants.