Data Engineer Jobs in Alexandria, VA

Builds and maintains Azure-based data infrastructure, including scalable ingestion pipelines, Databricks transformations, Azure Data Factory workflows, and event-driven integrations. Manages data lake storage, workspace governance, access controls, lineage, data quality, and pipeline reliability. Collaborates with IT, Finance, Operations, Digital, analysts, and developers to deliver curated datasets for reporting and analytics. Troubleshoots failures, documents architecture and runbooks, and supports migration to a centralized cloud-native data architecture.

Build and maintain scalable data platforms, stores, and ETL/ELT pipelines for structured and unstructured data. Develop software and scripts to retrieve, parse, process, and convert data into usable formats. Support analytics, reporting, monitoring, and platform operations while collaborating with multidisciplinary teams in an agile environment. Work may involve cloud, distributed computing, streaming, NoSQL, and data warehousing technologies.

Build and troubleshoot data ingestion, transformation, ETL/ELT, and integration pipelines; administer data management platforms; implement data quality, governance, metadata, lineage, and lifecycle processes; automate workflows; support secure data systems; and work directly with government mission users to onboard datasets, resolve issues, and enable analytical and AI applications.

Design, build, and operate scalable data pipelines and ETL for AI/ML. Create model-ready datasets (features, embeddings, prompts), implement storage layers (data lakehouse, vector stores), optimize low-latency retrieval for inference and RAG, tune search/indexing with ElasticSearch/OpenSearch, collaborate with analytics and product teams, and maintain CI/CD, version control, and Linux-based performance tuning.

Design, build, and maintain systems and architecture to collect, store, and analyze large volumes of data in support of DoD/IC programs. Work with stakeholders to enable data ingestion, storage, and analytics pipelines.

Design, build, and deploy scalable data pipelines and validation for government production environments. Establish and maintain schemas, optimize SQL, handle messy/disparate data sources, work with large (100GB+) workflows, use Databricks/Spark/Python, and support ML/predictive solutions while mentoring teammates and engaging with clients. Active Secret Clearance required.

As a Data Engineer at Booz Allen, you'll design and develop data pipelines, support data systems, and ensure data security while working in a team-focused environment.

Design, build, and maintain scalable data pipelines and platforms. Implement ETL/ELT workflows, parse and process structured/unstructured data, and support data platform scaling, monitoring, and operations while working on cross-functional agile teams.

The AWS Engineer will develop and support cloud-based solutions, migrate on-premise assets to AWS, optimize workloads, and guide the engineering team.

The Data Engineer will develop and implement software solutions for law enforcement, focusing on data management, system optimization, and effective communication with clients.

Design, build, and maintain scalable data pipelines and architectures to aggregate, clean, transform, and visualize large operational and intelligence datasets. Leverage Palantir, MSS, Python, Power BI, and AI/ML tools to produce dashboards, analytical products, predictive models, and briefings that support C-UAS operations, capability assessments, and acquisition decisions while ensuring data integrity, security, and governance.

Build, automate, and operate Azure-based enterprise data platform infrastructure. Responsibilities include Infrastructure as Code, CI/CD, Databricks administration, Azure data services, networking, security, monitoring, incident response, governance, FinOps, and reliability engineering. The role develops deployment standards, manages cloud environments and access controls, optimizes costs, supports data engineering teams, and documents operational procedures. Collaboration spans Data Engineering, IT Infrastructure, Cybersecurity, and business stakeholders.

Lead the architecture and implementation of scalable batch and streaming data pipelines across GCP, AWS, and Azure. Build production-grade Python data products, AI/ML, RAG, vector database, and agentic workflows; establish MLOps, LLMOps, CI/CD, containerization, observability, governance, and security practices. Optimize platform performance and cloud costs, resolve incidents, translate business strategy into technical solutions, set engineering standards, and mentor data engineers through hands-on coding.

The Big Data Engineer will build and code applications using Hadoop components and various programming languages, enhancing data management and analytics capabilities.

The Software Engineer will be responsible for data collection and infrastructure operations to support AI model training, collaborating with scientists on dataset roadmaps, and ensuring efficient data ingestion pipelines.

The Data Systems Engineer will support data integration, pipeline development, metadata tagging, and data governance in a fast-paced environment, collaborating with technical and business teams to ensure data quality and system interoperability.