Associate Senior Data Engineer
Job #:
req37869
Organization:
World Bank
Sector:
Information Technology
Grade:
GG
Term Duration:
3 years 0 months
Recruitment Type:
Local Recruitment
Location:
Washington, DC,United States
Required Language(s):
English
Preferred Language(s):
Closing Date:
8/10/2026 (MM/DD/YYYY) at 11:59pm UTC
Description
Do you want to build a career that is truly worthwhile? Working at the World Bank Group provides a unique opportunity for you to help our clients solve their greatest development challenges. The World Bank Group is one of the largest sources of funding and knowledge for developing countries; a unique global partnership of five institutions dedicated to ending extreme poverty, increasing shared prosperity and promoting sustainable development. With 189 member countries and more than 130 offices worldwide, we work with public and private sector partners, investing in groundbreaking projects and using data, research, and technology to develop solutions to the most urgent global challenges. For more information, visit www.worldbank.org
ITS Vice Presidency Context:
- Design and build dimensional and semantic data models on top of the curated data layer (Delta Lake/Unity Catalog) that translate raw data into business-ready tables
- Apply software engineering practices, including version control, modular design, and reusable macros, to data transformation code
- Own the AI-ready data layer at the enterprise for both structured and unstructured data ensuring transformations are documented as patterns/codified blueprints, tested, and repeatable
- Reduce duplication and inconsistency across data models by establishing canonical, reusable definitions for key business entities and metrics
- Define and maintain a single source of truth for enterprise metrics and business definitions, preventing divergent calculations across teams and tools
- Partner with Collibra-based governance work to ensure business metadata and technical metadata stay aligned as data moves from platform to consumption
- Establish data contracts between upstream data producers and downstream consumers, including AI agents and BI tools, to protect against silent schema or definition drift
- Implement automated data quality tests and validation checks as part of the transformation pipeline (not just at ingestion)
- Maintain living documentation of data models, lineage, and business logic so analysts, data scientists, and AI agents can self-serve with confidence
- Monitor data freshness, completeness, and accuracy of consumption-layer datasets and triage issues back to the appropriate upstream owner