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Data Systems Engineer Jobs (NOW HIRING)

RR

Data Systems/Solutions Engineer

Indianapolis, IN · On-site

$109K - $131K/yr

Position Summary The Data Systems / Solutions Engineer serves as a key technical contributor within the Regenstrief Data Services team, functioning as a full-stack DataOps/MLOps engineer supporting ...

Amentum

Wind Tunnel Data Systems Engineer

Hampton, VA · On-site

$110K - $132K/yr

The Data Systems Engineer is part of a team that supports wind tunnel facilities and will provide data support for data quality. The qualified candidate will provide operational and data support for ...

Avispa Technology

Systems Engineer

Santa Rosa, CA · On-site

$29/hr

Systems Engineer 37418801 * Hourly pay: $29/hr * Worksite: Leading electronic testing company ... Design and maintain Power BI dashboards, operational reports, and data visualization tools by ...

Avispa Technology

Systems Engineer

Santa Rosa, CA · On-site

$29/hr

Systems Engineer 37418801 * Hourly pay: $29/hr * Worksite: Leading electronic testing company ... Design and maintain Power BI dashboards, operational reports, and data visualization tools by ...

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All JobsData Systems Engineer Jobs

Data Systems Engineer information

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$53.5K

$127.2K

$167K

How much do data systems engineer jobs pay per year?

As of Jul 14, 2026, the average yearly pay for data systems engineer in the United States is $127,215.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,000.00 and $157,000.00 per year, depending on experience, location, and employer.

What engineers make $500,000 a year?

Senior data systems engineers with extensive experience, advanced skills in database management, cloud computing, and system architecture can earn $500,000 or more annually, especially in high-cost-of-living areas or within large technology companies. Achieving this level often requires specialized certifications, leadership roles, and a strong track record of managing complex projects.

What are some common challenges Data Systems Engineers face when integrating new data sources?

Data Systems Engineers often encounter challenges such as ensuring data compatibility across diverse formats, maintaining data integrity during migration, and managing system performance while integrating new sources. Collaborating closely with data analysts, software developers, and database administrators is key to anticipating and addressing these issues. Successful integration frequently requires thorough testing, robust error handling, and establishing clear data governance protocols to prevent inconsistencies or data loss.

What are Data Systems Engineers?

Data Systems Engineers are professionals who design, build, and maintain the infrastructure and systems that manage and process large volumes of data within an organization. They ensure data flows efficiently between databases, applications, and users, often working with technologies such as databases, data warehouses, and cloud platforms. Their responsibilities include optimizing data pipelines, ensuring data security, and supporting analytics and business intelligence initiatives. Data Systems Engineers collaborate closely with data scientists, software engineers, and IT teams to create reliable, scalable, and secure data environments.

What does a data systems engineer do?

A data systems engineer designs, develops, and maintains the infrastructure for storing, processing, and analyzing large data sets. They work with database systems, data pipelines, and cloud platforms, often using tools like SQL, Python, and Hadoop to ensure data availability, security, and performance. Strong problem-solving skills and knowledge of data architecture are essential for this role.

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

To excel as a Data Systems Engineer, you need strong skills in data architecture, database management, and programming, often backed by a degree in computer science or a related field. Familiarity with tools like SQL, Python, Hadoop, and cloud platforms, as well as certifications such as AWS Certified Data Analytics or Google Professional Data Engineer, is typically required. Exceptional problem-solving, collaboration, and analytical thinking help you design robust, scalable data solutions and communicate effectively with stakeholders. These skills and qualities are crucial for ensuring data integrity, optimizing system performance, and supporting organizational decision-making.

What is the difference between Data Systems Engineer vs Data Engineer?

AspectData Systems EngineerData Engineer
CredentialsBachelor's in CS, certifications like AWS, AzureBachelor's in CS, certifications like AWS, Azure
Work EnvironmentDesigning and maintaining data infrastructure, systems integrationBuilding data pipelines, ETL processes, data storage solutions
Industry UsageIT, tech companies, large enterprisesTech, finance, healthcare, any data-driven industry

Both roles require similar technical skills and certifications, often working in data infrastructure environments. Data Systems Engineers focus on designing and maintaining data systems, while Data Engineers primarily build and optimize data pipelines. The roles are complementary and often overlap in organizations managing complex data architectures.

Can I make 200K as a data engineer?

Data Systems Engineers with extensive experience, advanced skills in cloud platforms, big data tools, and certifications can potentially earn salaries of $200,000 or more, especially in high-cost-of-living areas or senior roles. Salary levels depend on factors such as location, industry, company size, and individual expertise.

What tech jobs pay 400,000 a year?

Data Systems Engineers, especially those with advanced skills in cloud computing, cybersecurity, or large-scale data management, can reach or exceed a $400,000 annual salary in senior or specialized roles. High-level positions in technology firms, finance, or consulting that require extensive experience, certifications, and leadership responsibilities may also offer compensation in this range.
More about Data Systems Engineer jobs
Who are the top companies hiring for Data Systems Engineer jobs? The top employers for Data Systems Engineer jobs are:
What states have the most Data Systems Engineer jobs? States with the most job openings for Data Systems Engineer jobs include:
What job categories do people searching Data Systems Engineer jobs look for? The top searched job categories for Data Systems Engineer jobs are:
Data Systems Engineer jobs near you
Infographic showing various Data Systems Engineer job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $127,215 per year, or $61.2 per hour.
Data Systems/Solutions Engineer

Data Systems/Solutions Engineer

RR

Indianapolis, IN • On-site

$109K - $131K/yr

Full-time

Life, Retirement, PTO

Re-posted 10 days ago

Job description

Position Summary
The Data Systems / Solutions Engineer serves as a key technical contributor within the Regenstrief Data Services team, functioning as a full-stack DataOps/MLOps engineer supporting research and analytics initiatives. This role is responsible for designing, building, and maintaining scalable, reliable data systems and pipelines that enable high-quality data ingestion, transformation, storage, and analysis.
The position emphasizes the development of robust, secure, and reproducible data infrastructure that supports data science, analytics, and AI-driven research. The Engineer applies modern software engineering and data engineering practices to ensure data assets are accessible, well-governed, and aligned with clinical and research requirements.
This position is a hybrid position with at least one (1) to two (2) days of onsite activity based on business needs. This position is located in downtown Indianapolis IN.
Essential Duties and Responsibilities
Data Systems Engineering and Operations:
  • Design, build, and maintain data platforms, pipelines, and services that support research, analytics, and AI/ML workloads.
  • Develop and maintain scalable data architectures using modern data warehouse/lakehouse patterns.
  • Ensure data systems are reliable, performant, and designed for long-term sustainability.
  • Implement and maintain ETL/ELT workflows, data validation, and quality monitoring processes.

DataOps / MLOps Enablement:
  • Implement CI/CD practices for data and ML workflows, including testing, version control, and environment promotion.
  • Support reproducible analytics and ML pipelines, including experiment tracking and model lifecycle considerations.
  • Apply best practices for monitoring, observability, and incident response across data systems.

Cloud, Security, and Governance
  • Design and maintain cloud-based data solutions using secure and scalable architectural patterns.
  • Apply data governance, access control, and auditing practices consistent with HIPAA-aligned research environments.
  • Ensure appropriate handling of sensitive data through de-identification, access management, and compliance controls.
  • Optimize performance and cost efficiency across compute and storage resources.

Clinical and Research Data Support
  • Work with clinical and research stakeholders to translate domain requirements into technical solutions.
  • Support integration and use of clinical and biomedical data standards (e.g., EHR data, HL7/FHIR, OMOP).
  • Produce well-documented data assets and technical specifications to support reuse and transparency.

Collaboration and Project Support
  • Collaborate with data engineers, researchers, analysts, and project managers to deliver high-quality solutions.
  • Contribute to project planning, estimation, and execution.
  • Serve as a technical resource to team members and stakeholders.
  • Document systems, workflows, and architectural decisions clearly and consistently.

Continuous Learning and Innovation
  • Maintain current knowledge of emerging tools, technologies, and best practices in data engineering and AI.
  • Leverage AI-assisted development tools responsibly to improve productivity and code quality.
  • Participate in continuous improvement efforts across systems, processes, and workflows.

Knowledge, Skills, and Abilities
Technical Knowledge:
  • Proficiency in modern data engineering concepts, including:
    • Data warehouse and lakehouse architectures
    • Dimensional modeling and data transformation patterns
    • SQL and at least one general-purpose programming language (e.g., Python)
  • Experience with CI/CD pipelines and automated testing for data and ML workflows
  • Familiarity with data quality frameworks, lineage tracking, and observability tools
  • Understanding of cloud platforms, identity and access management, and security best practices
  • Knowledge of clinical and biomedical data standards and research workflows preferred

Analytical and Problem-Solving Skills
  • Ability to analyze complex technical problems and implement effective solutions
  • Strong troubleshooting skills across data ingestion, transformation, and delivery layers
  • Ability to balance reliability, performance, and cost considerations

Communication and Collaboration
  • Strong written and verbal communication skills
  • Ability to document technical concepts clearly for both technical and non-technical audiences
  • Demonstrated ability to collaborate effectively in multidisciplinary teams

Education and Experience
  • Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field required; Master's degree preferred.
  • Minimum of three (3) years of professional experience in data engineering, systems engineering, or a related technical role.
  • Demonstrated experience in:
    • Data platform or data pipeline development
    • Cloud-based data system
    • SQL and programmatic data processing
    • DataOps or MLOps practices

Performance Expectations
  • Works independently within established guidelines and best practices.
  • Produces high-quality work with minimal supervision.
  • Demonstrates sound judgment and attention to detail.
  • Contributes to continuous improvement of tools, processes, and team effectiveness.

Physical Demands
  • Ability to work standard business hours with flexibility as needed.
  • Ability to sit or stand for extended periods.
  • Ability to operate a computer and standard office equipment.
  • Ability to lift and move materials up to 20 pounds as needed.
  • Ability to travel occasionally for meetings or training.

Work Environment
  • Hybrid office and research environment.
  • Fast-paced, deadline-driven setting.
  • Requires collaboration with internal teams and external partners.
  • Regular use of computers, communication tools, and office equipment.

BENEFITS OF WORKING HERE
  • Work with a variety of diverse professionals in the healthcare industry
  • Free parking
  • Paid holidays, vacation, and sick time
  • Group Life and Voluntary Term Life insurance
  • Long-term and Short-term Disability plans
  • Employee Assistance Program (EAP)
  • Flexible Spending Account (FSA)
  • 403b Retirement Plan with gracious employer contributions
  • Fitness program
  • Pet insurance
  • Qualified employer for loan forgiveness

Please note sponsorship and/or relocation are not available for this position.
REGENSTRIEF INSTITUTE REQUIRES ALL EMPLOYEES TO RECEIVE THE INFLUENZA VACCINATION ANNUALLY UNLESS APPROVED FOR EXEMPTION.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws.
For further information, please review the Know Your Rights notice from the Department of Labor.

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