Data Engineer

Data Engineer Washington, DC Pay From: $60.00 per hour Qualifications 7+ years of related experience; advanced degree preferred. Advanced working knowledge of SQL and experience working with relational database platforms including PostgreSQL, Microsoft SQL Server, and MySQL. Advanced working knowledge of Python, R, and other scripting languages used for data engineering and analytics. Working knowledge of additional programming and scripting languages such as Java, Scala, JavaScript, or Perl. Experience working with large‑scale data systems, including distributed computing, scalable data processing, data storage architecture, and optimization of high‑volume data workloads. Experience designing, developing, and automating ETL/ELT workflows and data integration pipelines. Experience building, optimizing, and maintaining scalable databases, data pipelines and data processing frameworks. Bachelor's degree in computer science, Information Technology, Engineering or a related technical field. Responsibilities Support Architecture, Technology, and Analytics (DATA) section with transforming the Research & Statistics (R&S) ingests, organizing, using, and visualizing data. Expand and optimize data and data pipeline architecture, as well as optimizing data flow and collection for economic policy and research teams. Be an experienced hands‑on data modeler with working knowledge of database design and administration, data pipeline building, and data wrangling, improving existing data systems and/or building them from the ground up. Support economists and technical experts and ensure optimal data delivery architecture is designed and developed. Be excited by the prospect of optimizing or re‑designing the R&S division's data architecture to support next generation of data initiatives. Work with workflow orchestration and pipeline automation tools such as Apache Airflow, Prefect, Dagster, or AWS Step Functions. Migrate workflows and data pipelines between on‑premises and cloud environments. Process, analyze, and integrate structured and unstructured data sources. Develop in Linux environments and use source control platforms such as GitLab and/or GitHub. Perform root cause analysis on internal and external data and business processes to answer business questions and identify opportunities for improvement. Design and communicate enterprise information architecture at conceptual, logical, and physical levels. Design and implement database, data lake, and enterprise data platform solutions. Provide strong hands‑on software engineering and implementation experience, including development, testing, and deployment of data applications and services. Exhibit excellent oral and written communication skills with strong customer service orientation, analytical, problem‑solving, and troubleshooting skills. Understand time series data and related analytical and forecasting techniques. Work in a research environment and/or with economic or financial data. Use NoSQL and graph database technologies. Develop, train, deploy, and maintain machine learning models. Work with cloud technologies such as AWS, Microsoft Azure, and Snowflake. Implement data warehouses utilizing Change Data Capture (CDC) methodologies. Implement and maintain CI/CD pipelines and DataOps platforms. Equity & Benefits Statement Quadrant is an affirmative action/equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, status as a protected veteran, or status as an individual with a disability. Healthcare benefits are offered to all eligible employees according to compliance mandated by the Affordable Care Act. #J-18808-Ljbffr Quadrant, Inc.