Job Title: Data Engineer
Location: Washington, DC onsite day 1
Job Type: contract
Duration: 12 months with possible extension
Mandatory Key skills: Azure ADF/DBT/Data bricks/ Python:-
Key Responsibilities
Design, develop, and maintain scalable data pipelines using Azure Databricks and Apache Spark
Implement complex data transformations using Spark SQL and Python (mandatory)
Build and manage analytics models using DBT Core / DBT Cloud, following analytics engineering best practices
Develop and orchestrate data ingestion and transformation workflows using Azure Data Factory
Ensure data quality, reliability, and performance across batch and near real-time pipelines
Integrate Databricks workloads with downstream reporting, analytics, and AI/ML use cases
Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions
Implement CI/CD pipelines and source control using Azure DevOps
Apply secure data engineering practices aligned with WBG data governance and compliance standards
Leverage AI-assisted tools to improve developer productivity, code quality, and operational efficiency
Participate in design reviews, code reviews, and knowledge sharing sessions
Mandatory Skills & Qualifications
Strong expertise in Spark SQL and Python programming (Mandatory)
Azure Databricks Data Engineer with at least one real-time implementation project
Hands-on experience with DBT Core and/or DBT Cloud
Experience building and managing pipelines using Azure Data Factory
Proficiency with Azure DevOps (Repos, Pipelines, CI/CD)
Experience using AI tools (e.g., AI-assisted development, data analysis, or automation tools)
Solid understanding of data warehousing concepts, ETL/ELT patterns, and distributed data processing
Ability to work in agile delivery models and collaborate with global teams
Preferred / Good to Have Skills
Databricks Certification (Associate or Professional) strongly preferred
Experience with Azure data ecosystem (ADLS Gen2, Azure SQL, Synapse, etc.)
Exposure to data governance, metadata management, and security frameworks
Familiarity with performance tuning and cost optimization on Databricks
Experience supporting analytics or AI/ML workloads on cloud platforms
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