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# Associate Senior Data Engineer

- **Company:** World Bank
- **Location:** Washington, DC, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Unity 3d, Agile Methodology, Artificial Intelligence, Data Analysis, Business Logic, Software Documentation, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Transformation, Data Visualization, Data Warehousing, Database Queries, Interoperability, Python (Programming Language), Modular Design, Raw Data, Reference Data, Power BI, Software Engineering, Tableau (Software), Unstructured Data, Data Layers, Build Management, Data Lakes, Collibra, Virtual Agents, Software Version Control, Databricks
- **Published:** July 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a4aa196086713431

## About the Role

* Education: Typically requires a Master's degree with 8 years of experience or a Bachelors degree with a minimum of 10 years of relevant experience, or equivalent combination of education and experience.

*
Role Specific Experience: 7+ years of experience in Data and/or Analytical engineering at enterprise scale

Certification Requirements:

 * SAFe or other relevant Agile certifications.
*
Industry-recognized certifications in Data and Analytics Engineering, particularly Databricks certifications.

Required Skills/Abilities:

 * Demonstrated expertise in analytics engineering practices, including dimensional data modeling, transformation pipeline design, and semantic layer development
*
Hands-on experience with dbt or comparable transformation frameworks, including testing, documentation, and CI/CD for data models
*
Strong SQL skills and working proficiency in Python for data transformation and automation
*
Experience working with modern lakehouse platforms (Databricks, Delta Lake, Unity Catalog) and translating curated data into consumption-ready models
*
Familiarity with data governance and cataloging tools (e.g., Collibra) and the ability to align technical metadata with business definitions
*
Experience defining and maintaining data contracts and metrics layers that serve both BI tools and AI/agentic consumers
*
Strong business acumen and communication skills, with the ability to translate stakeholder needs into scalable, well-documented data models
*
Working knowledge of BI, AI, and visualization tools (e.g., Tableau, Power BI, Lakeflow designer, Agentic AI) sufficient to understand how models will be consumed downstream
*
Understanding of data quality frameworks and observability practices to proactively catch issues before they reach business or AI consumers

## Description

* 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

Semantic Layer and Metrics Governance

 * 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

Testing, Documentation, and Data Quality

 * 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

Stakeholder Enablement and AI Readiness

 * Work directly with business analysts, data scientists, and product teams to understand use cases and translate them into well-structured, reusable data models
*
Prepare and structure datasets specifically for AI/agentic consumption, ensuring enterprise AI agents and accelerators draw from governed, high-quality data rather than ad hoc extracts
*
Build or support last-mile dashboards and self-service data products where the underlying model is the primary complexity
*
Act as the bridge between the Data Engineering team and business/AI consumers, reducing the load on data engineers to answer business-logic questions

Cross Cutting Analytics Standards and Interoperability

 * Define and publish enterprise standards for analytical data products, including naming conventions, business identifiers, metadata requirements, documentation standards, testing criteria, and quality controls to ensure consistency and interoperability across business units.
*
Embed analytics standards and governance requirements into platform capabilities through automated validation, testing, and compliance checks, ensuring adherence by design rather than through manual reviews and governance processes.
*
Build and maintain shared taxonomies, reference data, business entities, and semantic relationships that connect data products across business domains and enable cross-cutting analytics, reporting, AI, and self-service use cases.


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