• Deliver insights to help our clients turn data into action as a Data Engineer Senior at GDIT • Provide transformative solutions to clients’ big-data obstacles and help advance their missions • Ensure today is safe and tomorrow is smarter • Develop enterprise grade data platforms, services, and pipelines • Lead and architect migration of data environments with performance and reliability • Assess and understand the ETL jobs, workflows, BI tools, and reports • Address technical inquiries concerning customization, integration, enterprise architecture and general feature/functionality of data products • Contribute to the growth of our Data Exploitation Practice

• Design, build, and maintain scalable, high-quality data pipelines supporting both real-time and batch analytics workloads • Develop and optimize ETL/ELT processes using modern cloud data technologies and orchestration tools • Contribute to the design and evolution of lakehouse and warehouse data models that support analytics and reporting needs • Partner with business, design, test, and development teams to understand data requirements and improve data capture strategies • Help lead technical writing initiatives, including goal-setting, planning, and execution • Write, edit, and maintain clear, concise, and well-structured technical documentation • Create and improve documentation standards, organization, and knowledge-sharing processes • Organize and curate documentation to ensure it is intuitive, discoverable, and up to date • Advocate for consistent knowledge sharing across teams and studios • Research and propose improvements to documentation workflows and solutions to existing pain points • Apply modern engineering best practices to ensure reliability, scalability, and data quality across platforms

• Design, develop, and maintain data pipelines and datastores that support enterprise analytics, data science, and operational workloads. • Lead and support large-scale database migration initiatives, including on-premises to cloud migrations. • Monitor, analyze, and optimize the performance and stability of data layer services and platforms. • Ensure data integrity, quality, and compliance across pipelines and datasets. • Collaborate closely with peers across engineering, analytics, and technology teams. • Guide, coach, and mentor data engineers, BI developers, and analysts. • Design and implement enterprise-scale data solutions with long-term business impact. • Build and maintain data processing solutions using Python and/or Scala. • Work with a variety of data ingestion patterns, including SFTP, APIs, streaming, and batch processing. • Design and support database models optimized for analytical and reporting use cases. • Implement monitoring, alerting, and observability for data pipelines and infrastructure. • Maintain clear and comprehensive documentation of data architectures, pipelines, and processes. • Work within an Agile environment, collaborating through tools such as Jira and Git

• Design, build, and maintain scalable data pipelines and warehouse infrastructure to support analytics and reporting needs. • Develop and maintain clean, well-modeled datasets optimized for analytics and self-service reporting. • Partner with product managers and stakeholders to define key metrics, reporting requirements, and product analytics needs. • Build and maintain BI dashboards and reports that provide visibility into product performance, operational metrics, and customer behavior. • Ensure data quality, reliability, and governance across the analytics platform. • Integrate and maintain data ingestion pipelines from internal and external systems. • Improve the data warehouse performance and data modeling layer to enable faster and more reliable analytics. • Enable self-service analytics by creating curated datasets and semantic layers for internal teams. • Document datasets, metric definitions, and data lineage to improve data discoverability and trust in data. • Collaborate with engineering teams to ensure data is captured, modeled, and surfaced effectively for analytics use cases.