Data Engineer

Data Engineer

Washington DC (Day1 onsite)

The pay range for this role is $90k - $110k per annum including any bonuses or variable pay.

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 project implementation
• 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

Primarily looking At

• Azure Data Factory (ADF)
• Dbt
• Databricks
• Python
• Google Vertex

Preferred / Good to Have Skills

• Databricks Certification - Associate or Professional (strongly preferred)
• Experience with Azure data ecosystem (ADLS Gen2, Azure SQL, Synapse ‿ as applicable)
• 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