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About This Role
### Job Information
Industry
IT Services
Date Opened
07/24/2026
Job Type
Full time
City
Huntsville
State/Province
Alabama
Country
United States
Zip/Postal Code
35801
### About Us
Full Visibility LLC is a federally certified Small Business that delivers innovative technical solutions to address the nation’s most critical and sensitive missions. Founded in 2006, our extensive technical capabilities and expertise support federal and state government agencies with over 100 cleared technical experts specializing in mission solution areas of Digital, AI \& Analytics, Cyber, and Engineering.
With offices in the National Capital Region, Quantico, VA, and Huntsville, AL \- we deliver critical technical solutions to various federal government agencies and departments.
### Job Description
Full Visibility is seeking a Data Scientist/Data Engineer(Senior)to work in Huntsville, AL. Candidate *must*possess an *active* Top Secret/SCI Clearance with the ability to obtain a CI Poly successfully.
The Data Scientist/Data Engineer (Senior) will be responsible for reviewing data sets of many different types, identifying trends in the data, and finding methods for other users of the system to do a basic review of the data. The successful candidate will provide end\-users a better understanding of what they are reviewing, and help by teaching small groups of users, creating FAQ pages, and/or creating example documentation of best practices for data review. The Data Scientist/Engineer will help show the deficiencies in the currently available software and suggest changes so that end\-users can more easily access the data that is most important to them. The successful candidate will collaborate and coordinate with the software developers.
### Requirements
- ActiveTop Secret/SCI Clearance with the ability to obtain a CI Poly successfully.
- At least ten (10\+) years of experience as a data scientist or data engineer.
- Proficiency in Python, Java, or Scala, with experience working in AWS cloud environments.
- Knowledge of Natural Language Processing (NLP) concepts, techniques, and applications.
- Knowledge of statistical techniques and methodologies used to evaluate the performance and effectiveness of machine learning (ML) models.
- Experience using Git and GitLab for version control and CI/CD pipeline development and management.
\#CJ
This range is based on our market pay structures. However, individual salaries are determined by a variety of factors including, but not limited to: local market conditions, and candidate qualifications, such as skills, education, and experience. A reasonable salary range for this position is $140K to $220K### Benefits
About Full Visibility
Full Visibility LLC is a federally certified Small Business that delivers innovative technical solutions to address the nation’s most critical and sensitive missions. Founded in 2006, our extensive technical capabilities and expertise support federal and state government agencies with over 100 cleared technical experts specializing in mission solution areas of Digital, AI \& Analytics, Cyber, and Engineering.
Our technologists are recognized as industry leaders with demonstrated expertise in innovating and transforming our client environments in support of national security and law enforcement. Headquartered in Tysons, VA – with locations in Chantilly, VA, Quantico, VA, and Huntsville, AL – we have specialized experience supporting sensitive missions and an understanding of the niche skillsets required to overcome challenges in these environments. Our clients benefit from an enthusiastic and reliable team of talent with high employee morale and retention based on our FULL EMPLOYEE approach where each employee’s professional and personal well\-being is considered. Our focus on employee development, success, and morale is demonstrated by our consecutive top ranking as the Best Place to Work by the Washington Business Journal as a thirteen\-time honoree. Full Visibility has also been included on the Inc. 5000 Fastest\-Growing Private Companies in America list eight times since 2014\.
Full Visibility currently has over 20 active contracts with 100 billable staff supporting 21 clients in the Intelligence and Law Enforcement communities. As part of our commitment to partnerships, we are teammates with 13 prime contract holders. Additionally, Full Visibility holds contracts on several vehicles, such as GSA Multiple Award Schedule (MAS), DISA SETI, DIA SITE III, HCaTS, as well as several classified vehicles. Our portfolio includes experience serving the Department of Defense, Department of Homeland Security, Department of Justice, and Department of Veterans Affairs, as well as commercial clients.
Contact [email protected] for any questions or concerns related to this position or our company.
To review our company benefit offerings please visit https://www.fullvisibility.com/benefits.
Salary Context
This $140K-$220K range is above the median 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 Full Visbility, 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. Disclosed range: $140K to $220K.
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.
Full Visbility AI Hiring
Full Visbility has 1 open AI role right now. They're hiring across Data Engineer. Based in Huntsville, AL, US. Compensation range: $220K - $220K.
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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