Data Engineer
Summary
Data engineer supporting a secure government visual-analytics application: builds secure data ingestion pipelines, designs large relational databases, and develops AI/GenAI-driven data processing. Stack includes SQL/PostgreSQL, Python, TypeScript/React, FastAPI, Docker, Kubernetes, Temporal.io; an active TS clearance is required.
Location: Chantilly, VA
Security Clearance: Active TS or higher [Required]
Job Type: Full-Time
Target Salary Range*: $114,000 - $172,000
*This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary
Position Overview
Support advanced visual analytic applications by building data pipelines, designing large relational databases, and enabling high-performance analysis of structured and semi-structured data. Streamline processes, improve system performance, and strengthen secure data practices while mentoring junior team members and contributing to cross-team standards.
Key Responsibilities
Data Processing and Analytics
- Develop data processing workflows and analytics for desktop and web-based visual analytic tools.
- Support large-scale relational data analysis using visualization and high-performance computing.
Data Ingestion and Integration
- Build secure data ingestion pipelines using open-source, COTS, and GOTS technologies.
- Integrate new data systems with existing data warehouses.
- Improve system performance and functionality.
Database Design and Security
- Design and optimize enterprise relational databases.
- Establish standards for database operations and security.
- Contribute to system optimization and security posture improvements.
Mentorship and Cross-Team Standards
- Mentor junior team members.
- Contribute to cross-team standards.
Qualifications
Education
- Bachelor’s degree. [Required]
Experience
- Minimum of 8 years of experience in data engineering, database design, or related fields. [Required]
- Experience working with structured and semi-structured data and visualization technologies. [Required]
- Experience improving data workflows, performance, and security. [Required]
Skills
- Strong skills in relational database design, SQL, and ETL/ELT processing. [Required]
- Familiarity with high-performance computing and mixed-technology environments. [Required]
- Strong analytical, communication, and collaboration skills. [Required]
Application Requirements
- In-house custom-developed application.
Advanced AI, GenAI, and Multi-Model Engineering
- Multi-model and multi-modality orchestration involving reasoning, perception, memory, and tool execution.
- Temporal, spatial, geographic, quantitative, and computer vision AI implementation.
- Named Entity Recognition (NER), contextual entity extraction, and resolution.
- Multi-step retrieval and structured output generation.
- Complex data parsing and streamlined ingestion.
- Deep source citation and traceable analytic outputs.
Data Architecture and ETL
- Vector, graph, and multi-database orchestration.
- Automated ETL pipeline creation, scheduling, and optimization.
- Attribute-Based Access Control (ABAC) at the data layer.
- Provenance and end-to-end data lineage tracking.
- Schema-aligned ingestion and normalization of diverse structured and unstructured data.
Enterprise Infrastructure and Cross-Domain Solutions
- Air-gapped, hybrid, and on-premises operational environments.
- Container orchestration and distributed systems management using Kubernetes.
- Locally hosted GPU servers and hyperconverged nodes.
- Multitenant Cross-Domain Solutions (CDS).
Application Development and Integration
- UI, backend, and middleware engineering using TypeScript, React, Python, FastAPI, LanceDB, lance graph, PostgreSQL, Temporal.io, Docker, and Kubernetes.
- Integration of reasoning components, retrieval systems, databases, and AI / GenAI services.
- Use of modern programming languages and frameworks.
Preferred Qualifications
- Experience supporting federal or secure, multi-enclave environments.
- Background with advanced visualization platforms or high-volume data processing.
- Knowledge of data modeling, warehousing, and performance tuning.