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About This Role
APPLICATION INSTRUCTIONS:
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- CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply here, apply internally through Workday.
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- CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.
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- If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants .
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Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants .
POSITION SPECIFICS
We are seeking a Senior Data Engineer with deep expertise in database design, optimization, and data access strategies to support our growing data science and machine learning initiatives within the Computational Intelligence and Visualization Application Department in the Applied Research Laboratory (ARL) at Penn State . In this role, you will architect and optimize data systems that empower our data scientists to efficiently research, train, and deploy both traditional and ML\-based algorithms and applications.
The ideal candidate is a PostgreSQL expert who also brings hands\-on experience with other modern data storage technologies—such as NoSQL, graph, and time\-series databases —and can guide the organization in choosing the right tools and structures for each data use case.
Located in either State College, PA or Reston, VA
ARL is an authorized DoD SkillBridge partner and welcomes all transitioning military members to apply.
You will:
- Design and maintain scalable, high\-performance database solutions to support data science workflows and ML experimentation
- Partner with data scientists to understand data access patterns and develop storage strategies that accelerate analysis and model training
- Serve as the internal subject matter expert on PostgreSQL —including schema design, indexing, partitioning, and query optimization
- Evaluate and integrate alternative database technologies (e.g., MongoDB, Neo4j, Redis, Cassandra) where they provide clear advantages
- Lead efforts to optimize data pipelines for both structured and unstructured data used in algorithm development
- Ensure data integrity, security, and governance across storage systems
- Implement monitoring, automation, and performance\-tuning tools for all database environments
- Advise on data lifecycle management—balancing accessibility for R\&D with efficiency and compliance requirements
Required skills/experience includes:
- 5\+ years of experience in data engineering, database architecture, or related technical roles
- Expert\-level proficiency in PostgreSQL (query tuning, schema design, indexing, partitioning, replication)
- Strong understanding of data modeling , normalization vs. denormalization tradeoffs, and query optimization
- Experience with non\-relational databases (e.g., MongoDB, Cassandra, Neo4j, Redis, or DynamoDB)
- Familiarity with machine learning workflows and how data is consumed for training, evaluation, and deployment
- Experience with cloud database services (AWS RDS/Aurora, GCP Cloud SQL, Azure Database)
- Proficiency in SQL and one or more scripting languages (Python preferred)
- Excellent communication and collaboration skills—comfortable working closely with data scientists, ML engineers, and software developers
Preferred skills/experience includes:
- Experience architecting hybrid data ecosystems spanning relational, NoSQL, and analytical databases.
- Knowledge of data lake, warehouse, and feature store architectures (e.g., Snowflake, Redshift, BigQuery, Feast)
- Familiarity with ETL/ELT frameworks and data orchestration tools (e.g., Airflow, dbt)
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
Work location can be fully on\-site located in State College, PA or Reston , VA. Questions related to flexible work should be directed to the hiring manager during the interview process.
MINIMUM EDUCATION, WORK EXPERIENCE \& REQUIRED CERTIFICATIONS
If filled as R\&D Engineer \- Data Science (ARL) \- Principal Professional, this position requires:\&\#xa;Bachelor's Degree \- Engineering or Science\&\#xa;19\+ years of relevant experience\&\#xa;Required Certifications:\&\#xa;None\&\#xa;\&\#xa;If filled as R\&D Engineer \- Data Science (ARL) \- Advanced Professional, this position requires:\&\#xa;Bachelor's Degree \- Engineering or Science\&\#xa;5\+ years of relevant experience\&\#xa;Required Certifications:\&\#xa;None\&\#xa;\&\#xa;If filled as R\&D Engineer \- Data Science (ARL) \- Senior Professional, this position requires:\&\#xa;Bachelor's Degree \- Engineering or Science\&\#xa;14\+ years of relevant experience\&\#xa;Required Certifications:\&\#xa;None
ARL’s purpose is to research and develop innovative solutions to challenging scientific, engineering, and technology problems in support of the Navy, the Intel Community (IC), and other federal government customers.
FOR FURTHER INFORMATION on ARL, visit our website at www.arl.psu.edu .
BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies.
Notice regarding employment at the Applied Research Laboratory (ARL):\&\#xa;Employees must be eligible to obtain a government security clearance, participate in the ARL drug testing program, and comply with electronic and physical monitoring requirements applicable to federal contractors. ARL operates in a secure information environment involving Unclassified, Controlled Unclassified Information (CUI), and Classified information. Personal electronic devices brought onsite must be registered and may be restricted from certain areas. You must be a U.S. citizen to apply.
SALARY \& BENEFITS
The salary range for this position, including all possible grades, is $121,704\.00 \- $265,704\.00\.
Salary Structure \- Information on Penn State's salary structure
Penn State provides a competitive benefits package for full\-time employees designed to support both personal and professional well\-being. In addition to comprehensive medical, dental, and vision coverage, employees enjoy robust retirement plans and substantial paid time off which includes holidays, vacation and sick time. One of the standout benefits is the generous 75% tuition discount, available to employees as well as eligible spouses and children. For more detailed information, please visit our Benefits Page .
CAMPUS SECURITY CRIME STATISTICS
Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here .
EEO IS THE LAW
Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814\-865\-1473\.
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Salary Context
This $121K-$265K 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 Penn State University, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($193K) sits 5% above the category median. Disclosed range: $121K to $265K.
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.
Penn State University AI Hiring
Penn State University has 3 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, Research Engineer. Based in University Park, PA, US. Compensation range: $200K - $277K.
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.
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