Data Engineer

National Council on Aging•Arlington, VA

1d•Onsite

About The Position

Looking to innovate, grow, and make a positive impact on the lives of others? Join a nonprofit service and advocacy organization whose mission is to improve the lives of older adults, especially those who are struggling. We Are NCOA and we believe every person deserves to age well. The National Council on Aging, a national voice for every American’s right to age well, has an exciting opportunity for a Data Engineer, in its Arlington, VA/National Landing office. The Data Engineer will play a key role in strengthening NCOA’s data infrastructure, analytics capabilities, data governance practices, and business-process automation. This position designs, builds, documents, and maintains reliable data pipelines, data models, integrations, automated reporting solutions, and cataloged data assets that help internal teams access accurate, timely, and trusted information. The Data Engineer serves as a technical partner to business teams, data scientists, analysts, and application owners, translating organizational questions and operational needs into scalable data solutions. This role is also responsible for leading and maintaining NCOA’s data.world data catalog, supporting metadata standards, data discovery, business glossary practices, data stewardship, and adoption across the organization. The position requires strong data modeling skills, experience with relational databases and analytics platforms, comfort working across enterprise applications, and the ability to responsibly use AI-assisted tools such as OpenAI Codex, Microsoft Copilot, and AI-enabled features within the organization’s technology stack. Strong listening, documentation, customer service, and stakeholder-management skills are essential.

Requirements

Bachelor’s degree in information technology, Computer Science, Data Analytics, Information Systems, or a related field, or equivalent combination of education and relevant experience.
2–4 years of relevant experience in data engineering, analytics engineering, business intelligence, database administration, systems integration, or related work.
Experience with relational database management systems, SQL, data modeling, and analytics-ready dataset design.
Experience designing, maintaining, or improving ETL/ELT pipelines, data transformations, data integrations, or automated data workflows.
Experience with data cataloging, metadata management, data documentation, governance practices, or data stewardship workflows; experience with data.world preferred.
Exposure to reporting, dashboards, and analytics platforms such as Power BI, QuickSight, Tableau, Salesforce reporting, or comparable tools.
Experience gathering requirements, defining project scope, documenting decisions, and using data to generate business insights.
Ability to use AI-assisted tools responsibly to support development, documentation, analysis, testing, and productivity.
Strong customer service, stakeholder communication, and expectation-setting skills.
Excellent analytical, organizational, and problem-solving skills, including the ability to manage multiple priorities in a dynamic environment.
Strong critical thinking skills and a desire to take initiative, learn new technologies, and improve existing processes.

Nice To Haves

Understanding of ETL/ELT concepts, including data flow, enrichment, consolidation, change data capture, transformation, orchestration, and monitoring.
Understanding of database concepts such as referential integrity, indexing, keys, schemas, table metadata, query optimization, and data quality.
Hands-on experience with cloud data platforms or services such as AWS, Azure, SQL Server, S3, Glue, DynamoDB, RDS, Redshift, or comparable technologies.
Experience with Microsoft business and data tools such as Fabric, Power BI, Excel PowerPivot, Power Apps, Power Automate, Azure App Service, Microsoft 365 Copilot, or comparable tools.
Experience with Salesforce Sales Cloud, Experience Cloud, Nonprofit Success Pack, Marketing Cloud, or related CRM and marketing technologies.
Experience with workflow automation and work-management tools such as Zapier, Asana, Power Automate, or comparable platforms.
Experience with Git, CI/CD pipelines, testing, documentation, and deployment practices in an analytics or data-engineering environment.
Experience with containerization, infrastructure-as-code, APIs, or modern application integration patterns.
Familiarity with responsible AI practices, including appropriate review of AI-generated code, protection of sensitive data, documentation of assumptions, and validation of outputs.
Knowledge of nonprofit operations, aging services, benefits access, advocacy, or mission-driven organizations.

Responsibilities

Partners with internal business teams to define key business questions, translate requirements, and build datasets, automations, and reporting solutions that address organizational needs.
Design, build, test, document, and maintain data pipelines, data models, and analytics-ready datasets.
Serve as the technical and operational lead for NCOA’s data.world data catalog, including catalog configuration, metadata standards, data asset documentation, business glossary support, lineage and ownership practices, stakeholder onboarding, and ongoing adoption across teams.
Develop and maintain ETL/ELT processes, data transformations, data quality checks, and workflow automations across organizational systems.
Use AI-assisted development and productivity tools, including OpenAI Codex, Microsoft Copilot, and embedded AI features in enterprise platforms, to improve engineering, documentation, testing, analysis, and automation workflows while following security and responsible-use expectations.
Design, maintain, and troubleshoot workflow automations using tools such as Zapier and Asana, ensuring data movement, task creation, notifications, and cross-system handoffs are reliable, documented, and aligned with business processes.
Prototype, validate, and productionize reporting, automation, and data-product solutions in partnership with stakeholders.
Collaborate with data science, analytics, enterprise applications, and business stakeholders to improve data access, data literacy, governance, and operational decision-making.
Document technical solutions, data definitions, lineage, processes, automations, and support procedures.
Analyze, understand, and create ETL automation for data sources that serve the needs of internal users and data scientists.
Other duties as assigned.