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Decision Support Engineer Jobs (NOW HIRING)

Apex Informatics

Production Support Engineer

Minneapolis, MN · On-site

$43.75 - $57.25/hr

Job Title- Production Support Engineer Location- Minneapolis Minnesota 55415 (Hybrid) Duration- 24 ... and decision making skills Ability to prioritize work, meet deadlines, achieve goals, and work ...

FIS Amount

Production Support Engineer

Chicago, IL

$73K - $80K/yr

The Production Support Engineering team plays a key role at FIS Amount by ensuring production ... Documentation of all relevant events, getting status reports while driving decision-making and ...

SEF Energy

Field Support Engineer

Odessa, TX · On-site

Dynamic, Situational Adaptability, Quality Decision Making, Resourcefulness What is an IT Operations Field Support Engineer? The Field Support Engineer is responsible for partnering with the ...

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How much do decision support engineer jobs pay per hour?

As of May 28, 2026, the average hourly pay for decision support engineer in the United States is $39.87, according to ZipRecruiter salary data. Most workers in this role earn between $29.57 and $46.63 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Decision Support Engineer, and why are they important?

To thrive as a Decision Support Engineer, you need a solid background in data analytics, systems engineering, and problem-solving, often supported by a degree in computer science, engineering, or a related field. Familiarity with business intelligence tools (such as Tableau or Power BI), database management systems, and programming languages like SQL and Python is typically required. Strong communication, analytical thinking, and collaboration skills help you translate complex data into actionable insights for stakeholders. These competencies are essential for effectively supporting organizational decision-making and driving data-driven strategies.

How does a Decision Support Engineer typically collaborate with data scientists and business stakeholders?

Decision Support Engineers play a key role in bridging the gap between technical data teams and business decision-makers. They work closely with data scientists to interpret complex models and ensure that data-driven insights are clearly communicated and actionable. Additionally, they engage with business stakeholders to understand their needs, translate them into technical requirements, and develop user-friendly tools or dashboards that support strategic decisions. This collaborative environment requires strong communication skills, adaptability, and a solid understanding of both technical and business perspectives.

What is a Decision Support Engineer?

A Decision Support Engineer is a professional who designs, develops, and maintains systems that help organizations make data-driven decisions. They work with large datasets, analytical tools, and business intelligence platforms to provide actionable insights to management and stakeholders. Their role often includes integrating data from multiple sources, creating dashboards, and implementing algorithms that support strategic business decisions. Decision Support Engineers collaborate closely with IT, data science, and business units to ensure that decision-making processes are efficient, accurate, and aligned with organizational goals.

What is the difference between Decision Support Engineer vs Data Analyst?

AspectDecision Support EngineerData Analyst
Required CredentialsBachelor's in Engineering, Computer Science, or related field; knowledge of data systemsBachelor's in Statistics, Mathematics, or related field; proficiency in data analysis tools
Work EnvironmentTechnical teams, engineering projects, data systemsBusiness units, reporting, data visualization
Industry UsageManufacturing, logistics, technologyFinance, marketing, healthcare
Common Search/ComparisonDecision Support Engineer vs Data Analyst

The Decision Support Engineer focuses on developing systems and tools to aid decision-making processes, often working with engineering and technical teams. In contrast, Data Analysts primarily interpret data to generate reports and insights for business decisions. While both roles require data skills, Decision Support Engineers typically have a stronger technical background in systems and engineering, whereas Data Analysts focus more on data interpretation and visualization.

Infographic showing various Decision Support Engineer job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $82,930 per year, or $39.9 per hour.

Data Engineer - Clinical Decision Support Solutions

Omni Inclusive

West Sacramento, CA • Hybrid

$118.80K - $142.70K/yr

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Description: Beckman Coulter is seeking a team member to join our Microbiology R&D Development Science functional team. In this role you will develop and maintain end-to-end data and machine Learning pipelines for clinical and verification studies. We're looking for associates who thrive in a team-oriented, goal focused environment.
The Data Engineer for Beckman Coulter Diagnostics is responsible for development and implementation of end-to-end Ops pipelines to support ML model deployment throughout the entire ML lifecycle. This position is part of the data science located in Sacramento, California and will be a hybrid role. The data engineer will be a part of the development science functional group and report to the data science manager. If you thrive in a cross functional team and want to work to build a world-class biotechnology organization-read on.
Responsibilities
• Collaborate with stakeholders to understand data requirements for ML, Data Science and Analytics projects.
• Assemble large, complex data sets from disparate sources, writing code, scripts, and queries, as appropriate to efficiently extract, QC, clean, harmonize and visualize Big Data sets.
• Write pipelines for optimal extraction, transformation, and loading of data from a wide variety of data sources using Python, SQL, Spark, AWS 'big data' technologies.
• Develop and Design data schemas to support Data Science team development needs
• Identify, design, and implement continuous process improvements such as automating manual processes and optimizing data delivery.
• Design, Develop and maintain a dedicated ML inference pipeline on AWS platform (SageMaker, EC2, etc.)
• Deployment of inference on a dedicated EC2 instance or Amazon SageMaker
• Establish a data pipeline to store and maintain inference output results to track model performance and KPI benchmarks
• Document data processes, write data management recommended procedures, and create training materials relating to data management best practices.
Required Qualifications
• BS or MS in Computer Science, Computer Engineering, or equivalent experience.
• 5-7 years of Data and MLOps experience developing and deploying Data and ML pipelines.
• 5 years of experience deploying ML models via AWS SageMaker, AWS Bedrock.
• 5 years of programming and scripting experience utilizing Python, SQL, Spark.
• Deep knowledge of AWS core services such as RDS, S3, API Gateway, EC2/ECS, Lambda etc
• Hands-on experience with model monitoring, drift detection, and automated retraining processes
• Hands-on experience with CI/CD pipeline implementation using tools like GitHub (Workflows and Actions), Docker, Kubernetes, Jenkins, Blue Ocean
• Experience working in an Agile/Scrum based software development structure
• 5-years of experience with data visualization and/or API development for data science users

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