1

Quantitative Data Engineer Jobs in Arkansas (NOW HIRING)

Applied research, data analysis, data programming, data visualization, and data management, report ... quantitative and qualitative research, statistical and content analysis. * Survey research ...

Select the most appropriate qualitative and quantitative methodologies to answer complex business ... Present findings to merchants, product developers, and senior leadership * Leverage AI and emerging ...

Senior Data Analyst

Little Rock, AR · On-site

$82K - $104K/yr

Bachelor's degree in a quantitative field (e.g., Statistics, Economics, Engineering, Computer Science). * Strong SQL and Python skills; proficient in Excel for data analysis. * Experience creating ...

Lead Development Engineer

Springdale, AR · On-site

$90K - $119K/yr

Implementing data protection and security measures. Customer Interaction: * Interacting with ... Computer Science, Computer Engineering, Information Systems, Quantitative or Engineering Field ...

Lead Development Engineer

Springdale, AR · On-site

$90K - $119K/yr

Implementing data protection and security measures. Customer Interaction: * Interacting with ... Computer Science, Computer Engineering, Information Systems, Quantitative or Engineering Field ...

next page

Showing results 1-20

Quantitative Data Engineer information

What is a quantitative data engineer?

A Quantitative Data Engineer is a professional who designs, builds, and maintains data infrastructure that supports quantitative analysis, typically in finance or technology sectors. They work closely with quantitative analysts and data scientists to ensure efficient data pipelines, data quality, and high-performance systems for processing large datasets. Their responsibilities include developing ETL processes, optimizing databases, and implementing data models to support research and trading strategies. Strong programming skills, expertise in big data technologies, and knowledge of quantitative methods are essential for this role.

How does a quantitative data engineer typically collaborate with data scientists and quantitative analysts on projects?

Quantitative Data Engineers work closely with data scientists and quantitative analysts to design, build, and optimize data pipelines that support complex modeling and analytics. They are often responsible for ensuring data quality, scalability, and efficient data processing, enabling analysts to focus on developing models and extracting insights. Regular collaboration includes translating analytical requirements into technical solutions, troubleshooting data issues, and iterating on data infrastructure to support evolving project needs. This teamwork fosters an environment where technical and analytical expertise complement each other, leading to more robust and actionable results.

What are the key skills and qualifications needed to thrive as a quantitative data engineer, and why are they important?

To excel as a Quantitative Data Engineer, you need strong proficiency in programming (such as Python, R, or C++), advanced mathematical and statistical knowledge, and a relevant degree in computer science, mathematics, or a related field. Experience with big data tools (like Spark, Hadoop), cloud platforms, and data pipeline systems, as well as familiarity with financial data sets, is typically required. Analytical thinking, detail orientation, and effective problem-solving skills distinguish top performers in this role. These competencies are critical for efficiently transforming complex data into actionable insights and supporting robust quantitative models in data-driven environments.

What is the difference between Quantitative Data Engineer vs Data Scientist?

AspectQuantitative Data EngineerData Scientist
Primary FocusBuilding data pipelines, data infrastructure, and ensuring data qualityAnalyzing data, creating models, and deriving insights
Skills & ToolsSQL, Python, Spark, ETL processes, data architectureStatistics, machine learning, Python/R, data visualization
CredentialsComputer science, engineering, or related degrees; certifications in data engineeringStatistics, data science, or related degrees; certifications in data analysis or machine learning
Work EnvironmentData engineering teams, data infrastructure projectsData analysis teams, research, and modeling projects

While both roles work closely with data, Quantitative Data Engineers focus on building and maintaining data systems, whereas Data Scientists analyze data to generate insights and models. They often collaborate but have distinct skill sets and responsibilities within data-driven organizations.

What are popular job titles related to Quantitative Data Engineer jobs in Arkansas?

For Quantitative Data Engineer jobs in Arkansas, the most frequently searched job titles are:

What job categories do people searching Quantitative Data Engineer jobs in Arkansas look for?

The top searched job categories for Quantitative Data Engineer jobs in Arkansas are:

What cities in Arkansas are hiring for Quantitative Data Engineer jobs?

Cities in Arkansas with the most Quantitative Data Engineer job openings:

Infographic showing various Quantitative Data Engineer job openings in Arkansas as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 15% Part Time, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Lead Development Engineer -AI-Assisted Engineering

Springdale, AR


Tyson Foods
Food and Drink Manufacturing • 10K+ employees

6.3

Company rating: 6.3 out of 10

Based on 553 frontline employees who took The Breakroom Quiz

297th of 443 rated food and drinks producers

Respectful managers


$90K - $119K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 28 days ago


Job description

Job Details:

The Lead Development Engineer is a senior-level professional with advanced expertise across all skill dimensions. They lead and oversee complex projects, provide technical guidance and mentorship to teams, and drive the adoption of best practices and technologies across application development, testing, integration, data engineering, and infrastructure. They establish (not just follow) the software product engineering best practices for the organization and are responsible for encoding those standards into AI coding agents at scale. This position consolidates responsibilities formerly held by the specialized Lead Development Engineer, Lead Integration Engineer, Lead Quality Engineer, and Lead IT Data Engineer roles.

Essential Duties and Responsibilities

Design software, integration, data, and testing architecture; deliver high-complexity tasks across all skill dimensions as part of a team. Own the definition and continuous improvement of AI coding agent configurations, prompt libraries, and automated review standards for the organization. Design AI-assisted workflows that enable junior developers to deliver production-ready output across the entire stack; evaluate and select AI tools and platforms for the engineering organization. Drive adoption of AI-assisted development practices across teams; measure and report on AI-assisted development effectiveness and quality outcomes. Design, code, and debug applications in various software languages or tools; design software architecture as expert in multiple front-end and back-end frameworks. Develop software solutions by studying information needs, conferring with users, and studying systems flow, data usage, and work processes; establish and enforce coding standards, design patterns, and architecture principles. Design testing architecture for the enterprise; demonstrate expertise in automated testing frameworks integrated with Version Control, DevOps & CI/CD. Utilize chaos engineering to validate system resilience; develop and implement comprehensive test strategies and plans. Lead performance, load, and stress testing at enterprise scale; establish quality metrics and reporting frameworks for stakeholders and management. Design and oversee risk-based testing strategies across the organization. Design integration architecture for the enterprise; demonstrate expertise in designing and architecting integration solutions across systems and applications.

(Preferred / Nice-to-Have)

Expert in APIs and Web Services (RESTful, SOAP, gRPC), middleware and message brokers, ETL processes; expert in integration platforms and tools (SnapLogic, MuleSoft, Dell Boomi, Apache Camel, Talend). (Preferred / Nice-to-Have) Lead the resolution of complex integration escalations; drive preventive measures. (Preferred / Nice-to-Have) Help drive overall data strategy including designing and implementing comprehensive data architectures and visualization solutions; architect end-to-end data solutions with strong emphasis on cloud cost optimization. (Preferred / Nice-to-Have) Drive data strategy alignment with organizational goals; ensure enterprise-level data governance and security including advanced protections. (Preferred / Nice-to-Have) Lead integration of big data technologies, data streaming solutions, and advanced visualization tools in large-scale environments; oversee complex data pipeline orchestration. (Preferred / Nice-to-Have) Mentor engineers in SQL, scripting, and advanced data visualization techniques. (Preferred / Nice-to-Have) Expert in Infrastructure as Code (IaC); design patterns for cloud infrastructure management. Expert in DevOps and CI/CD (Jenkins, GitLab CI, CircleCI, or equivalent); establish organizational pipeline standards. Expert in Security (Authentication, Authorization, Encryption); design security architecture. Expert in Monitoring and Alerting (Splunk, ELK Stack, or equivalent); design observability strategy; lead cloud cost optimization and resource management. Lead and influence in collaborative meetings with peers and stakeholders; lead interactions with stakeholders to create requirements and demonstrate work. Document code, architecture, integration processes, APIs, and outcomes; establish software product engineering best practices across all skill dimensions. Review and correct others. Scope: Enterprise. Work independently with limited supervision; act as a knowledge resource within the team; lead and define priorities for projects or processes. Apply judgment and experience to identify resolution; make timely decisions and take action. Perform other assigned job-related duties that align with our organization's vision, mission, and values and fall within your scope of practice.

Qualifications

Education: Bachelor's, Master's Degree, or a significant amount of relevant experience. Computer Science, Computer Engineering, Information Systems, Quantitative or Engineering Field preferred. Experience: 5+ years of relevant and practical experience.

Special Skills

Programming (expert in multiple languages or software tools). Expert in multiple front-end and back-end frameworks. Expert in Data Engineering (pipelines, warehousing, streaming, governance, visualization) (preferred). Design patterns for Infrastructure as Code (IaC). Design patterns with Version Control, DevOps & CI/CD. Expert in automated testing; utilizes chaos engineering. Expert in APIs, Web Services, Middleware, Message Brokers, ETL (integration & data patterns preferred). Expert in Security (Authentication, Authorization, Encryption). Proficient in Monitoring and Alerting. Expert with Cloud Services (AWS, Azure, Google Cloud). Expert with Version Control (Git). AI agent architecture, prompt engineering, and AI-assisted SDLC design. Technical Writing and Diagramming. Analysis (Technical, Business, or Data).

Soft Skills

Leadership: Driving engineering strategy and best practices across the organization; providing technical guidance and mentorship. Stakeholder Management: Building and maintaining relationships with internal and external partners. Strategic Vision: Aligning engineering initiatives with long-term business objectives. Communication: Presenting complex technical concepts to executive leadership. Mentorship: Fostering the growth and development of the engineering team across all skill dimensions. Decision-Making: Making informed choices about architecture, tools, and AI adoption. Change Management: Guiding the organization through engineering transformations and the shift to AI-assisted development. Emotional Intelligence: Understanding and motivating team members.

**Not eligible for visa sponsorship now or in the future**

**Not eligible for relocation assistance**

Relocation Assistance Eligible:

No

Work Shift:

1ST SHIFT (United States of America)

Certain roles at Tyson require background checks. If you are offered a position that requires a background check you will be provided additional documentation to complete once an offer has been extended.

Hourly Applicants ONLY -You must complete the task after submitting your application to provide additional information to be considered for employment.


The successful candidate(s) must be willing and able to perform the physical requirements of the job with or without a reasonable accommodation.


Tyson is an Equal Opportunity Employer. All qualified applicants will be considered without regard to race, national origin, color, religion, age, genetics, sex, sexual orientation, gender identity, disability or veteran status.


We provide our team members and their families with paid time off; 401(k) plans; affordable health, life, dental, vision and prescription drug benefits; and more.


If you would like to learn more about your data privacy rights and how you may use that information, please read our Job Applicant Privacy Notice here.


Unsolicited Assistance: Tyson Foods and its subsidiaries do not accept unsolicited support from external recruitment vendors for open positions within the United States. Any resumes or candidate profiles submitted by recruitment vendors or headhunters to any employee or applicant tracking system at Tyson Foods or its subsidiaries, without a valid written request and search agreement approved by HR, will be considered the property of Tyson Foods. No fees will be paid if the candidate is hired due to an unsolicited referral.



What Tyson Foods employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom