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Quantitative Data Engineer Jobs in Arkansas (NOW HIRING)

Murphy USA

Data Scientist I

El Dorado, AR · On-site

EDUCATION AND EXPERIENCE Bachelor's degree in a quantitative field like Computer Science, Statistics, Engineering, Science, or Mathematics. 1 to 3 years of related experience in data mining and ...

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Quantitative Data Engineer information

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 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.

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.

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 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:
Quantitative Data Engineer jobs near you
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.

Manager of Data Engineering

Summit Utilities Inc

Fort Smith, AR • On-site

Full-time

Medical, Dental, Vision

Posted 11 days ago

Job description

Join our growing team and discover why Summit Utilities, Inc. continues to earn national and regional recognition as an employer of choice. Our recognitions include Best Places to Work in Maine (2019–2025); Best Places to Work in Arkansas (2020, 2023, 2025); Best Places to Work in Oklahoma (2022–2025); Best Places to Work in Missouri (2023 and 2026); Best Places to Work in Colorado (2025); Forbes America’s Best Small Employers (2023); and, most recently, Proud and Purposeful Employer (2026).


Summit is a growing natural gas utility that’s committed to delivering reliable energy to homes and businesses in Arkansas, Colorado, Maine, Missouri, Oklahoma, and Texas. Being part of the Summit team means embracing excellence and innovation, committing to safety each and every day, and doing all that we can to serve each other, our customers, and the communities where we live. We aim to bring warmth and energy to everything we do.


We are pleased to announce an exciting opportunity for a Manager of Data Engineering (SGL35) to join our team. This hybrid role may be based in one of our offices in Little Rock, Fort Smith, or Fayetteville, Arkansas.

POSITION SUMMARY

A Manager of Data Engineering leads and mentors the data engineering team, overseeing the design, development, implementation, and maintenance of scalable and robust data infrastructure and pipelines. Successful candidates are strategic thinkers, possess strong leadership qualities, and are adept at managing complex data projects in a fast-paced environment. They are responsible for ensuring data quality, integrity, and accessibility across the enterprise. A Manager of Data Engineering collaborates daily with IT leadership, data engineers, scientists, analysts, and business stakeholders to define data strategy, address data needs, and drive data-informed decision-making. They must foster a culture of innovation and continuous improvement within the data engineering team.

PRIMARY DUTIES AND RESPONSIBILITIES

  • Lead, manage, and mentor a team of data engineers and analysts, fostering their professional growth and development.
  • Oversee the architecture, design, and implementation of enterprise-level data warehousing, data lakes, and data pipeline solutions.
  • Define and enforce data engineering best practices, standards, and methodologies.
  • Collaborate with cross-functional teams, including data science, business intelligence, and application development, to understand data requirements and deliver effective solutions.
  • Ensure the reliability, scalability, and performance of data infrastructure and systems.
  • Develop and implement strategies for data quality management, data governance, and data security.
  • Manage the full lifecycle of data engineering projects, including planning, execution, monitoring, and delivery.
  • Evaluate and recommend new technologies, tools, and techniques to enhance data engineering capabilities.
  • Drive automation of data processes to improve efficiency and reduce manual intervention.
  • Communicate effectively with technical teams and business stakeholders regarding project status, risks, and outcomes.
  • Establish and monitor key performance indicators (KPIs) for the data engineering team and data systems.
  • Troubleshoot and resolve complex data-related issues in a timely manner.
  • Develop and manage the budget for the data engineering department.
  • Stay current with industry trends and advancements in data engineering and big data technologies.
  • Champion a data-driven culture within the organization.

EDUCATION AND WORK EXPERIENCE

  • Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related quantitative field is preferred, or a combination of education and equivalent experience.
  • 7+ years’ experience in data engineering, with a proven track record of designing and implementing complex data solutions.
  • 3+ years’ experience in a leadership or managerial role, successfully leading and developing data engineering teams.

KNOWLEDGE, SKILLS, ABILITIES

  • Strong leadership, team-building, and interpersonal skills.
  • Expertise in data modeling, ETL/ELT development, and data warehousing concepts (e.g., Kimball, Inmon).
  • Proficiency in programming languages such as Python, Scala, or Java.
  • Extensive experience with big data technologies (e.g., Apache Spark, Hadoop, Kafka, Flink).
  • Deep understanding of cloud-based data platforms and services (e.g., AWS Redshift, S3, Glue; Azure Synapse, Data Lake Storage, Data Factory; Google BigQuery, Cloud Storage, Dataflow).
  • Proficient in SQL and experience with various database technologies (e.g., relational, NoSQL, columnar).
  • Experience with data pipeline orchestration tools (e.g., Apache Airflow, Prefect, Dagster).
  • Solid understanding of data governance, data quality, data lineage, and data security principles and practices.
  • Excellent problem-solving, analytical, and critical thinking skills.
  • Strong project management skills, with the ability to manage multiple priorities and deadlines.
  • Exceptional communication and presentation skills, with the ability to convey complex technical concepts to non-technical audiences.
  • Experience with DevOps and DataOps methodologies and tools (e.g., CI/CD, infrastructure-as-code).
  • Familiarity with business intelligence tools (e.g., Power BI, Tableau) and their data integration needs.
  • Strategic mindset with the ability to align data engineering initiatives with overall business objectives.

The above statements are intended to describe the general nature and level of work being performed by employees assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and/or skills required of all personnel so classified.

Summit offers competitive pay and medical/dental/vision and other benefits that provide flexibility, choice, and support to our employees when they need it most. We understand that home and family are essential pieces of your life, and our benefits are designed to support you both at work and at home.

Summit Utilities, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or protected veteran status and will not be discriminated against on the basis of disability or veteran status.