Data Engineer and Analyst

DevTech is a mission-driven firm specializing in innovative, data-driven solutions that help governments, civil society, and the private sector strengthen public financial systems, unlock investment, and make informed decisions in complex environments. We combine advanced analytics, contextual insight, and blended finance strategies to deliver practical, forward-looking solutions worldwide. Guided by our core values of integrity, innovation, evidence-driven impact, resilience, and collaboration, DevTech works transparently and in close partnership with clients and communities to achieve measurable results and lasting impact. DevTech is looking for a Data Engineer and Analyst to work as an institutional contractor on its Analytics, Data, Visualization, and Information Services 2.0 (ADVISE 2.0) contract with the United States Department of State. The Data Engineer and Analyst will work with the Office of Global Food Security (GFS) to support Humanitarian Assistance (HA) across the State Department. The Data Engineer and Analyst will support the Humanitarian Data, Assessment, & Coordination unit within GFS’ Technical Quality & Assurance Division. The Humanitarian Data, Assessment, & Coordination unit strives to create and share decision support products with humanitarian assistance decision makers to improve their understanding of humanitarian needs, internal and external humanitarian data, and to improve the quality and targeting of global humanitarian assistance in crisis settings. Overall, we are seeking a data-savvy professional who can bridge engineering and analysis. This hybrid Data Engineer and Data Analyst role involves building and maintaining data systems, exploring and interpreting complex humanitarian datasets, and empowering colleagues to make informed, evidence-based decisions. The ideal candidate combines technical expertise with intellectual curiosity, strong analytical skills, and a passion for using data to support humanitarian outcomes. Responsibilities Data Engineering & Infrastructure (≈60%) Design, develop, and maintain scalable data pipelines and ETL/ELT workflows to support humanitarian operational and analytical needs. Build and optimize data models, schemas, and cloud-based data architectures to support dashboards, machine learning workflows, and advanced analytics. Respond quickly to database problems that arise and carry out periodic maintenance and troubleshooting. Monitor system performance by performing regular tests, troubleshooting, and integrating new features. Integrate heterogeneous humanitarian data sources. Implement data quality, validation, and monitoring processes to ensure accuracy, reliability, and transparency. Maintain an awareness of trends and developments in database maintenance. Maintain documentation, metadata, and data governance standards in alignment with state department policies. Occasionally, respond quickly to data needs for responses to rapid onset disasters or urgent decision-making requests. Data Analysis, Insights, & Decision Support (≈40%) Explore and analyze complex humanitarian datasets to identify trends, anomalies, risks, and opportunities. Produce actionable insights that inform policy, resource allocation, program design, and crisis response. Support predictive analytics or light machine-learning workflows where appropriate. Communicate analytical findings clearly to technical and non-technical audiences. Data Leadership, Guidance & Collaboration Serve as a subject-matter resource for data best practices, including how to access, interpret, and apply available datasets. Work closely with the team business analyst and HA decision makers to understand their data needs and translate them into analytical or technical solutions. Provide training, informal guidance, and practical tools to staff who rely on data for decision-making. Demonstrate flexibility by taking on adjacent responsibilities as needed in a small, fast moving team environment to ensure project goals are met. Engage in continuous learning about the humanitarian sector. General As needed, may serve on temporary detail within the bureau to meet operational needs during staff shortages. Duties performed while on detail will be aligned with the Team’s existing duties and responsibilities and will be directly related to the scope of work provided. In compliance with the Department of State and contractor policies and procedures, consistently model behaviors that demonstrate a commitment to fostering a non-hostile work environment free of discrimination, bias, unfairness, exclusion, offensive behaviors, and harassment of any kind. Demonstrate consistent accountability for adherence to and knowledge of laws, executive orders, and Department of State’s policies which prohibit Equal Employment Opportunity (EEO) and non-EEO infringements as well as the Agency’s zero tolerance for sexual misconduct, including harassment, exploitation, and abuse of any kind. Understand reporting criteria and report up in a timely manner. Requirements Bachelor’s degree in Data Science, Computer Science, Statistics, Information Systems, or a related field. Master’s degree or equivalent experience preferred. Hands-on experience in both: Data engineering (pipelines, ETL/ELT, SQL, cloud tools) Data analysis (exploratory analysis, dashboarding, basic data science methods) Proficiency with SQL and modern programming/scripting languages (Python preferred). Familiarity with cloud platforms (e.g. AWS, Databricks) and common data engineering tools. Strong analytical thinking, problem-solving skills, and intellectual curiosity. Ability to explain complex concepts to diverse stakeholders. A team player but able to work independently. Multi-tasking and time-management skills, with the ability to prioritize tasks. Excellent interpersonal skills. Ability to work well in a diverse team. The candidate must be a U.S. citizen to qualify for the required U.S. government security clearance for this project. This position’s place of performance is Washington D.C. Remote work has been authorized for this position. #J-18808-Ljbffr