Senior Data Engineer

Expression•Shirlington, VA

6h•$180,000 - $200,000

About The Position

Expression is seeking a Senior Data Engineer to design, develop, and integrate secure enterprise data services supporting advanced AI and LLM capabilities for the Department of Homeland Security. The position supports secure, mission-focused AI applications in the Government’s existing AI environment. The engineer will implement governed integrations with enterprise platforms such as Palantir, Databricks, and Elasticsearch so AI agents and retrieval-augmented generation (RAG) workflows can securely access, process, and reference authorized information. The role requires hands-on experience with enterprise data integration, API development, data modeling, secure data access, SQL, and Python. The engineer will work across complex data ecosystems, address data quality and performance issues, and collaborate with AI architects and engineers, platform owners, cybersecurity teams, and Government stakeholders. The position is based in Shirlington, Virginia, and requires a Secret clearance with eligibility for TS/SCI.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Software Engineering, Data Science, or a related technical field, or equivalent relevant professional experience.
At least 7 years of professional experience in data engineering, software engineering, or enterprise data integration, including at least 3 years of hands-on experience developing enterprise data pipelines, APIs, or governed data services.
Strong SQL and Python skills for querying, transformation, API integration, scripting, debugging, and automated testing.
Demonstrated experience designing, developing, and maintaining enterprise data integrations, including RESTful APIs, data connectors, schema mappings, and secure application-to-application data exchange.
Hands-on experience integrating with at least one of Palantir, Databricks, or Elasticsearch, including platform APIs, data access mechanisms, or query interfaces.
Experience with structured, semi-structured, and unstructured enterprise data, including data modeling, schema management, transformation, and retrieval optimization.
Experience implementing secure, permission-aware data access, including authentication, authorization, identity propagation, sensitive-data handling, and source-level access restrictions.
Experience implementing data quality, validation, lineage, and traceability controls for enterprise data integrations.
Experience developing data retrieval services or pipelines supporting analytics, intelligent search, AI applications, or RAG workflows.
Experience troubleshooting complex data integration issues, including API failures, schema inconsistencies, query performance, authorization errors, and data quality problems.
Experience with Git, code reviews, CI/CD pipelines, automated testing, and configuration management.
Ability to translate technical and data governance requirements into secure integration designs, data contracts, test cases, and engineering documentation.
Strong written and verbal communication skills and ability to collaborate with AI engineers, software developers, platform owners, and Government stakeholders.
Secret clearance required, with eligibility for TS/SCI.

Nice To Haves

Hands-on integration experience with two or more of Palantir, Databricks, and Elasticsearch.
Experience with Palantir data objects, relationships, APIs, or governed data-access capabilities.
Experience with Databricks data products, SQL query services, data governance, lineage, or managed analytical datasets.
Experience with Elasticsearch query APIs, index or alias management, document retrieval, and field-level access restrictions.
Experience implementing RAG data pipelines, semantic or vector retrieval, document parsing, embeddings, and context preparation for LLM applications.
Experience supporting AI/ML or generative AI applications requiring secure enterprise-data retrieval, source attribution, and permission-aware context assembly.
Experience with cloud-native data engineering and containerized applications using AWS, Azure, Docker, Kubernetes, or comparable technologies.
Familiarity with Infrastructure as Code, DevSecOps practices, and automated deployment pipelines.
Experience implementing controlled data synchronization, incremental updates, audit logging, and approved write-back operations across enterprise systems.
Experience supporting Federal Government, Department of Defense, Intelligence Community, Department of Homeland Security, or similarly regulated data environments.
Familiarity with Government cybersecurity, privacy, data governance, access control, and software authorization requirements.
Experience supporting technical demonstrations, Government acceptance testing, operational pilots, or production data integration sustainment.

Responsibilities

Design, develop, and maintain secure enterprise data integrations connecting Government-approved platforms with AI applications and agentic AI workflows.
Develop and integrate APIs, data connectors, and retrieval services for Palantir, Databricks, Elasticsearch, and other authorized enterprise data sources.
Implement access and retrieval capabilities for structured, semi-structured, and unstructured data, including records, documents, relationships, and analytical data products.
Design and maintain schema mappings, transformations, and context-preparation processes for RAG, intelligent search, AI-assisted document generation, and source-grounded analytical workflows.
Implement secure, permission-aware data access controls, including authentication, authorization, identity propagation, case-purpose restrictions, and source-level permissions.
Preserve source identifiers, timestamps, metadata, access restrictions, and document references to ensure data provenance, lineage, and traceability.
Develop data validation, quality, and consistency controls to identify missing, stale, malformed, conflicting, or unauthorized records.
Optimize data retrieval and API performance, including query efficiency, response latency, and reliable access to enterprise data services.
Collaborate with AI engineers to integrate authorized data into LLM and RAG workflows and ensure generated outputs can be traced to supporting records and comply with access controls.
Design and implement automated unit, integration, and regression tests for connectors, APIs, schema mappings, data quality, and authorization boundaries.
Implement secure error handling, audit logging, and observability to monitor integrations, troubleshoot failures, and support operational reliability.
Support controlled data synchronization and approved write-back operations, including input validation, human approval, and safeguards against duplicate or unauthorized updates.
Collaborate with platform owners, cybersecurity teams, and Government stakeholders to define interface specifications, resolve integration challenges, and maintain compliance with data governance requirements.
Create and maintain interface control documents, data-flow diagrams, schema mappings, API specifications, test results, operational runbooks, and technical documentation.
Support CI/CD integration, configuration management, software releases, and regression testing as schemas, interfaces, or access requirements change.
Participate in technical design and code reviews, engineering planning, and cross-functional troubleshooting.
Support technical demonstrations, prototype evaluations, Government acceptance testing, and operational pilot deployments.