WBG Pioneer - AI Data Engineer Intern

Job Description:

Every World Bank-financed project depends on systems that track how money is spent, how risk is managed, and how communities are protected. ITSFD builds those systems-this internship is a chance to work on them directly. Background/Organizational Context The World Bank Group (WBG) finances development projects across the globe through three distinct institutions, the International Bank for Reconstruction and Development and the International Development Association (IBRD/IDA), the International Finance Corporation (IFC), and the Multilateral Investment Guarantee Agency (MIGA). The Information and Technology Solutions (ITS) Vice Presidency of the World Bank Group delivers a wide portfolio of technology products and services that enable the WBG's development mission. Within ITS, the Fiduciary Operations unit (ITSFD) is responsible for digital solutions that support Procurement, Financial Management, and Environmental and Social Safeguards across World Bank operations. About the Team: Procurement The Procurement team sits within ITS Fiduciary Operations (ITSFD) and is responsible for the digital systems that support procurement activities across World Bank-financed projects. Its flagship platform is STEP (Systematic Tracking of Exchanges in Procurement), an end-to-end online system used for procurement planning, tracking, and document management across the full procurement lifecycle; from planning through contract management. STEP is used by Task Team Leaders, Procurement Specialists, and borrowers alike and supports transparency and accountability through machine-readable reporting and open contracting data. The intern will gain hands-on experience in how a global institution plans and validates data migration for a complex platform modernization. The role offers exposure to agile delivery, data governance, QA practices, UAT preparation, stakeholder coordination, and the practical realities of moving from legacy systems to a modern digital platform. Duties and Responsibilities Collect data from databases, APIs, cloud storage, files, and other sources. Clean and transform raw data by handling missing values, duplicates, incorrect formats, and outliers, and build and maintain ETL/ELT pipelines using Python, SQL, and data-engineering tools. Perform data validation and quality checks to ensure accuracy and consistency. Work with large datasets using platforms such as Snowflake, Databricks, Spark, or cloud services. Create and maintain data tables, schemas, feature datasets, and data documentation. Assist with feature engineering for machine-learning models. Support vector databases, embeddings, and Retrieval-Augmented Generation pipelines for generative AI applications. Collaborate with data engineers, data scientists, ML engineers, and software developers. Participate in code reviews, team meetings, testing, and technical documentation