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About This Role
An Iron EagleX Opportunity
Iron EagleX, a GDIT company, contributes to the U.S. government’s mission of protecting our nation and enables our customers to make quicker decisions and act faster than our adversaries.
Clearance Level
Top Secret/SCI
Category
Data Science and Data Engineering
Location
Arlington, Virginia
*(Onsite Workplace)*
Key Skills For Success
Apache Airflow
Database Optimization
Extract Transform Load (ETL)
Git
Structured Query Language (SQL)
##### REQ\#:RQ226448
##### Public Trust:None
##### Requisition Type:Regular
##### Your Impact
Own your opportunity to support our nation's defense. Make an impact by connecting and securing critical operations across the globe, keeping our country safe and secure.
Job Description
-------------------
YOUR IMPACT
Own your opportunity to work with the largest government agency in the nation. Make an impact by advancing the Department of War’s mission to keep our country safe and secure.
OUR COMPANY
Iron EagleX (IEX), a wholly owned subsidiary of General Dynamics Information Technology (GDIT), delivers agile IT and Intelligence solutions. Combining small\-team flexibility with global scale, IEX leverages emerging technologies to provide innovative, user\-focused solutions that empower organizations and end users to operate smarter, faster, and more securely in dynamic environments.
JOB DESCRIPTION
Iron EagleX is seeking a Senior Principal AI Data Engineer to support our AI team in Crystal City, VA. In this role, you will design, build, and operate data pipelines that ingest, store, and process high\-volume, multi\-source data primarily for modern AI/ML processes. You will partner with software, analytics, and product teams to create model\-ready datasets (including features, embeddings, and prompts), implement scalable storage layers (such as a data lakehouse and vector stores), and enable low\-latency retrieval for query, inference, and retrieval\-augmented generation (RAG).
MEANINGFUL WORK AND PERSONAL IMPACT
As a Senior Principal AI Data Engineer, you will turn raw, multi\-source data into reliable, high\-performance inputs that directly power AI models and advanced analytics. Your work will make it faster and easier for teams across engineering, analytics, and products to develop, deploy, and improve AI capabilities by ensuring datasets are ready and accessible.
JOB DUTIES (INCLUDE BUT ARE NOT LIMITED TO)
- Design, develop, and implement scalable data pipelines and ETL processes using Apache Airflow, with a focus on data for AI
- Build and tune search and retrieval capabilities using ElasticSearch/OpenSearch, including indexing strategies, schema mappings, and relevance/performance optimization.
- Enable low\-latency retrieval for AI inference and RAG applications by optimizing data access patterns, caching approaches, and index refresh strategies.
- Collaborate with analytic teams to define requirements, schemas, and interfaces for downstream consumption.
- Use Git for version control, peer code reviews, CI/CD workflows, and reproducible pipeline deployments across environments.
- Operate within Linux environments and perform performance tuning across pipeline components, storage layers, and compute resources.
REQUIRED SKILLS:
- Experience with Apache Airflow for workflow orchestration.
- Strong programming skills in Python.
- Experience with ElasticSearch/OpenSearch for data indexing and search functionalities.
- Understanding of vector databases, embedding models, and vector search for AI applications.
- Expertise in event\-driven architecture and microservices development.
- Hands\-on experience with cloud services (e.g. MinIO), including data storage and compute resources.
- Strong understanding of data pipeline orchestration and workflow automation.
- Working knowledge of Linux environments and database optimization techniques.
- Strong understanding of version control with Git.
WHAT YOU’LL NEED TO SUCCEED
- Clearance: Current TS/SCI Clearance with current or willingness to obtain CI polygraph
- Experience:10\+ years of related experience
- Education: Bachelor’s degree in Computer Science, Software Engineering, or a related field (or equivalent experience)
- Role requirements: Work is onsite in Crystal City, VA with optional CONUS travel
- Due to US Government Contract Requirements, only US Citizens are eligible for this role
GDIT IS YOUR PLACE
At GDIT, the mission is our purpose, and our people are at the center of everything we do.
- Growth: AI\-powered career tool that identifies career steps and learning opportunities
- Support: An internal mobility team focused on helping you achieve your career goals
- Rewards: Comprehensive benefits and wellness packages, 401K with company match, competitive pay and paid time off
- Community: Award\-winning culture of innovation and a military\-friendly workplace
OWN YOUR OPPORTUNITY
Explore a career at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your passion for the mission and delivering results. \#iexjobs \#iexpriority
Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans
\#iexjobs
### Work Requirements
Years of Experience
10 \+ years of related experience
- may vary based on technical training, certification(s), *or* degree
Certification
Travel Required
None
Citizenship
U.S. Citizenship Required
### Salary and Benefit Information
The likely salary range for this position is $174,250 \- $235,750\. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.
### Our Identity Verification Process
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.
### About Our Work
We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50\+ countries worldwide, offering leading mission\-ready capabilities in AI, cloud, cyber and software development.
Join our Talent Community to stay up to date on our career opportunities and events at gdit.com/tc.
*Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans*
Salary Context
This $174K-$235K range is above the 75th percentile for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At General Dynamics Information Technology, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($205K) sits 11% above the category median. Disclosed range: $174K to $235K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 13 open AI roles right now. They're hiring across Data Scientist, Data Engineer, AI/ML Engineer, AI Software Engineer. Positions span Remote, US, Arlington, VA, US, Chantilly, VA, US. Compensation range: $154K - $287K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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