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About This Role
Job Description Data Engineer/AI Solution Engineer Req Id: 2315 State/Province: Delaware, Maryland City: Elkton Business: Gore Performance Solutions Category: Engineering, Scientists & Technicians Sub-Category: Engineering Experience Level: Experienced Work Arrangement: Hybrid Posting Start Date: 9/28/26 Posting End Date: 10/13/26 About the Role
We are hiring a Data Engineer/AI Solution Engineer to join the Gore Performance Solution (GPS) Applied Research (AR) team and build the database architecture and AI/ML foundation needed to make Gore's materials, characterization, and fitness-for-use (FFU) data searchable, reusable, and ready for predictive analytics.
In this role, you will take end-to-end technical ownership of the Gore materials data and its supporting storage platform – from database architecture and data pipelines through cloud infrastructure, secure application access, and applied AI/ML. You will partner closely with AR scientists and engineers, the Innovation team, and GPS IT to translate materials, process, and product data and knowledge into scalable digital solutions. This role is expected to grow into a shared GPS Innovation capability supporting multiple research and NPD programs.
This position will be located at our facility in Elkton, Maryland or Newark, Delaware with the possibility of a hybrid work arrangement.
Pay and Benefits:
- $110,000 - $153,000 per year
- Roles above $150k are eligible for the variable pay program each fiscal year, based on defined business performance metrics.
- Factors considered in extending an offer within this pay range include (but not limited to) education, experience, knowledge, skills, and abilities.
- Medical, Dental and Vision benefits effective on Day 1 of employment! We offer a comprehensive total rewards package, including competitive benefits. More details are available at www.gore.com/careers/benefits
Responsibilities
- Design, build, and maintain PostgreSQL database architecture, including permissions, views, optimized query rules, and fine-grained access control.
- Develop standardized Python libraries and automated data-ingestion pipelines to capture materials, characterization, and fitness-for-use (FFU) testing data at scale.
- Build and maintain REST APIs and user-friendly access tools – including web applications – that make data, models, and AI-enabled tools available to scientists and engineers.
- Own the platform's cloud architecture and deployment on Microsoft Azure, including Azure Container Apps and Azure Static Web Apps, plus monitoring, scaling, and production configuration.
- Architect and build responsive frontend applications (Angular or React) that give researchers an intuitive, well-designed interface to data and analytical tools.
- Implement authentication and authorization using Microsoft Entra ID (App Registrations, tokens, and Entra Groups), extending role-based access control (RBAC) down to the database level.
- Partner with GPS IT to integrate security groups, pipelines, APIs, and applications into enterprise infrastructure with appropriate security, access controls, and long-term support.
- Set up automated CI/CD pipelines and Infrastructure as Code to enable reliable, repeatable deployment of the platform.
- Design and implement AI/ML-enabled workflows – such as predictive analytics, process-structure-property models, and LLM-assisted tools – that support faster, higher-quality NPD decisions.
- Translate complex materials science, characterization, and regulatory/quality requirements into structured data models, workflows, and system logic.
- Collaborate cross-functionally with AR scientists/engineers, the Innovation team, and GPS IT to identify opportunities, define requirements, and deliver scalable solutions.
- Document platform architecture and solutions, as well as establish reusable frameworks and standards to enable this capability to scale beyond the initial data family.
Required Qualifications
- Bachelor's degree in Engineering, Computer Science, Data Engineering, Software Engineering, or a related technical field.
- Minimum of 1 year of demonstrated experience in database architecture and design, with solid proficiency in PostgreSQL, including permissions, views, optimized query rules, and access control.
- Minimum of 1 year of demonstrated experience developing AI/ML models or workflow-based automation/decision-support solutions.
- Software development experience in Python (or another general-purpose object-oriented language such as C++ or Java), and proficiency with Git.
- Experience building responsive, multi-page web applications with Angular or React.
- Hands-on experience with Microsoft Azure.
- Deep understanding of Microsoft Entra ID (Azure AD), including configuring App Registrations, tokens, and Entra Groups for Role-Based Access Control (RBAC).
- Experience developing and maintaining REST APIs, and comfort working with asynchronous execution states and data flows.
- Strong problem-solving skills and the ability to operate independently in ambiguous, cross-functional environments, influencing without direct authority.
- Ability to travel up to 5%.
Desired Qualifications
- Familiarity with materials science, characterization methods, or fitness-for-use (FFU) testing data, ideally in a regulated or advanced-materials environment.
- Experience with web-based visualization tools such as vega-lite and D3, and building data-dense engineering dashboards.
- Experience setting up automated CI/CD pipelines (e.g., GitHub Actions or Azure DevOps).
- Experience writing Infrastructure as Code (IaC) using Azure Bicep or Terraform.
- Experience with cloud data warehouses and analytics engines such as Databricks or Snowflake.
- Experience with AI-assisted workflows, LLM-based tools, or prompt-driven solution design.
- Experience with Microsoft Power Platform (Power Apps, Power Automate, Dataverse).
Provide sustained technical ownership and continuity for the platform as materials, data, and business needs evolve. *
This position offers a hybrid work arrangement in the country from which the Associate is employed. Eligibility is dependent on the responsibilities of the role and business needs, and the Associate must agree to comply with Gore’s work arrangement policies.
What We Offer
Our success is based on the capability and creativity of our Associates, and we are proud to offer a comprehensive and competitive total rewards program that supports your everyday and helps you build your tomorrow.
We provide benefits that offer choice and flexibility and promote overall well-being. And in keeping with our belief that every Associate should share in the collective success of the enterprise; we provide a distinctive Associate Stock Ownership Plan in each country as well as potential opportunities for “profit-sharing”. Learn more at gore.com/careers/benefits
Gore is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, national origin, ancestry, age, status as a qualified individual with a disability, genetic information, pregnancy status, medical condition, marital status, sexual orientation, status as a protected veteran, gender identity and expression, and any other characteristic protected by applicable laws and regulations.
Gore is committed to a drug-free workplace. All employment is contingent upon successful completion of drug and background screening. Gore will consider qualified applicants with criminal histories, e.g., arrest and conviction records, in a manner consistent with the requirements of applicable laws.
Gore requires all applicants to be eligible to work within the United States. Gore generally will not sponsor visas unless otherwise noted on the position description.
Salary Context
This $110K-$153K range is below the median for Data Engineer roles in our dataset (median: $150K across 25 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 8,943 AI roles we're tracking, Data Engineer positions make up 1% of the market. At W. L. Gore & Associates, 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 $177,825 based on 160 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($131K) sits 26% below the category median. Disclosed range: $110K to $153K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
W. L. Gore & Associates AI Hiring
W. L. Gore & Associates has 1 open AI role right now. They're hiring across Data Engineer. Based in Elkton, MD, US. Compensation range: $153K - $153K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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