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About This Role
About the Organization
Now is a great time to join Redhorse Corporation. We are a solution\-driven company delivering data insights and technology solutions to customers with missions critical to U.S. national interests. We’re looking for thoughtful, skilled professionals who thrive as trusted partners building technology\-agnostic solutions and want to apply their talents supporting customers with difficult and important mission sets. About the Role
Redhorse Corporation is at the forefront of transforming how the government leverages data and technology. We are seeking a highly skilled Artificial Intelligence (AI) and Data Engineering SME to support the Office of the Under Secretary of War for Intelligence and Security (OUSW(I\&S)) Chief Data and Artificial Intelligence Officer (CDAIO).
This position sits within the OUSW(I\&S) Innovation and Data (INSID) team, a fast\-paced, problem driven environment focused on disrupting legacy processes and transforming how intelligence and data capabilities are developed, governed, and operationalized across the Defense Intelligence and Defense Security Enterprise (DISE).
This is not a routine staff role. The successful candidate will thrive in ambiguity, enjoy continuous intellectual challenge, and actively engage senior leaders to turn complex AI and data concepts into actionable, mission relevant outcomes.
### Key Responsibilities
- Develop and deliver performance reports, strategic assessments, decisional papers, and action memos to enable actionable insights for senior leaders.
- Deliverables include responses to CATMS tasks, executive summaries of AI forums, proposals for advanced analytics and predictive modeling applications, requirements documentation, programmatic reviews for AI\-related initiatives, and assessments of partnerships between DoW components, intelligence agencies, and industry.
- Collaboration with OUSW(I\&S) and CDAO on congressional responses and memos for use for the Under Secretary of War for Intelligence and Security (USW(I\&S).
- Conduct comprehensive mapping of organizational activities, business processes, governance structures, and workflows to support the design, development, and deployment of AI tools that integrate intelligence data across the DoW Intelligence Community. Deliver initial organizational mapping within 120 days of contract start date, followed by monthly updates to ensure alignment with evolving mission requirements.
- Conduct mission analysis and risk assessments to identify resourcing requirements and inform strategic decision\-making. Align delivery of Planning, Programming, Budgeting, and Execution (PPBE) products with established AI timelines, ensuring compliance with organizational priorities and deadlines (estimated monthly).
### Required Experience
- Active Top Secret SCI security clearance is required.
- Bachelor's Degree and 15 years of experience in a combination of military/government/civilian, or contractor experience with Government or DoW; experience with the Office of the Secretary of War (OSW) is highly preferred.
- Enterprise\-wide knowledge and familiarity with AI work, engineering, integration, and/or acquisition lifecycle and management are required.
- Experience with contract and Statement of Work writing is required.
### Desired Experience
- Education: Master's Degree or Juris Doctorate from an accredited college or university, preferably in computer science/engineering or mathematics.
- AI Fundamentals: Solid understanding of machine learning, neural networks, and natural language processing concepts.
- AI Infrastructure: Conceptual knowledge of AI enabling hardware, including technical tradeoffs.
- Technical Proficiency: Familiarity with programming languages (e.g., Python), data structures, and data science methodologies.
- Mathematical Foundation: A firm understanding of mathematics, including statistics, calculus, probability, and linear algebra, and how it is applied to AI algorithms and models.
- Intelligence Warfighting Domain Knowledge: Deep understanding of all\-source analysis and single\-source intelligence as well as how intelligence supports military and national security operations.
- Communication Skills: Strong oral and written communication skills, with the ability to explain complex technical topics to non technical senior leaders
- Intellectual Curiosity: Demonstrated interest in understanding how systems work and improving them
- Strategic Level DoW Experience: At least one year at OSW/OSD, the Joint Staff, a Combatant Command, and/or a Military Service headquarters staff.
- Commercial AI Experience: At least one year experience in a technical role in commercial industry.
The salary range provided for this position represents the anticipated base salary for successful candidates. Actual compensation will be determined based on a variety of factors, including relevant experience, education, certifications, skills, security clearance level, geographic location, market conditions, and internal equity. In addition to base salary, eligible employees may participate in Redhorse's comprehensive benefits programs and may be eligible for performance\-based or other incentive compensation, where applicable.
Redhorse Corporation is an equal opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability, or any other protected class.
If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to access job openings or apply for a job on this site as a result of your disability. You can request reasonable accommodations by contacting Talent Acquisition at Talent\[email protected]
Redhorse Corporation shall, in its discretion, modify or adjust the position to meet Redhorse’s changing needs. This job description is not a contract and may be adjusted as deemed appropriate in Redhorse’s sole discretion.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $170K-$190K range is above the 75th percentile for Data Engineer roles in our dataset (median: $150K across 15 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 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Redhorse Corporation, 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 $178,800 based on 40 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $170K to $190K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Redhorse Corporation AI Hiring
Redhorse Corporation has 1 open AI role right now. They're hiring across Data Engineer. Based in Arlington, VA, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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