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About This Role
Responsibilities:
PeopleTec is currently seeking an AI Data Engineer to support our El Paso, TX location. Duties:* Develop and maintain Palantir software solutions to support business needs.
- Design, test, and implement AI models to improve business processes.
- Stay up to date with the latest advances in machine learning infrastructure and operations to develop and maintain efficient systems.
- Drive implementation, testing, releases, and monitoring for data processing pipelines and machine learning products.
- Collaborate with cross\-functional teams to identify business needs and provide technical solutions.
- Analyze and interpret data to extract meaningful insights using Palantir and AI tools.
- Ensure data quality and integrity throughout the development process.
- Conduct research on new Palantir and AI technologies to stay current with industry trends.
- Provide technical guidance and support to other team members.
- Apply knowledge of Agile software development using Scrum.
- Demonstrate familiarity with data structures, storage systems, cloud infrastructure, front\-end frameworks, and other technical tools.
- Analyze business workflows and apply algorithmic thinking to design solutions that meet specific customer needs.
- Exhibit strong communication and interpersonal skills.
- Work independently and make decisions with minimal supervision.
Qualifications:
Required Skills/Experience:* Proven experience in Palantir software development and Artificial Intelligence.
- Experience designing and implementing ML infrastructure to train and deploy large language models.
- 3\+ years’ experience in software development.
- 2\+ years’ experience developing and implementing AI models using machine learning algorithms.
- Familiarity with Palantir software, including Palantir Gotham, Palantir Foundry, and Palantir Metropolis.
- Strong knowledge of programming languages such as Java, Python, JavaScript, and React.
- Proficiency with major deep learning frameworks.
- Ability to analyze complex data sets and extract meaningful insights.
- Excellent problem\-solving and analytical skills.
- Strong communication and teamwork skills.
- Must be authorized to work in the US.
- Must be able to obtain and hold a DoD Secret Security Clearance, with the possibility to upgrade to Top Secret/SCI.
- Willing to work in El Paso, TX.
Education Requirements:* Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (4 years of experience may be considered in lieu of a degree).
Estimated Time To Hire:* August 2026
Overview:
*People First. Technology Always.* PeopleTec, Inc. is an employee\-owned small business founded in Huntsville, AL that provides exceptional customer support by employing and retaining a highly skilled workforce. Culture: The name "PeopleTec" was deliberately chosen to remind us of our core value system \- our people. Our company's foundation was built on placing our employees and customers first. With an award\-winning atmosphere, we have matured into a company that boasts the best and brightest across multiple technical fields. Career: At PeopleTec, we value your long\-term goals. Whether it's through our continuing\-education opportunities, our robust training programs, or our "People First" benefits package, PeopleTec truly believes that our best investments are our people. *Come Experience It.**\#cjpost \#dpost* *EEO Statement* *PeopleTec, Inc. is an Equal Employment Opportunity employer and provides reasonable accommodation for qualified individuals with disabilities and disabled veterans in its job application procedures. If you have any difficulty using our online system and you need an accommodation due to a disability, you may use the following email address, [email protected]* *and/or phone number (256\.319\.3800\) to contact us about your interest in employment with PeopleTec, Inc.* *All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, citizenship, ancestry, marital status, protected veteran status, disability status or any other status protected by federal, state, or local law. PeopleTec, Inc. participates in E\-Verify.*
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 3,823 AI roles we're tracking, Data Engineer positions make up 1% of the market. At PeopleTec, 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 $208,300 based on 266 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
PeopleTec AI Hiring
PeopleTec has 1 open AI role right now. They're hiring across Data Engineer. Based in El Paso, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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