Data Engineer

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