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Data Ops Engineer Jobs in Illinois (NOW HIRING)

Harris Associates

Data Analyst, Data Ops

Chicago, IL · On-site

The Data Ops team is responsible for delivering scalable data, reporting and operational analytics ... Bachelor's degree in Data Analytics, Engineering, Computer Science, Business, or a related field ...

Harris | Oakmark

Data Analyst, Data Ops

Chicago, IL · On-site

... Ops role. The Data Analyst will ensure the accurate and timely delivery of data and analytics ... Required : • Bachelor's degree in Data Analytics, Engineering, Computer Science, Business, or a ...

Amazon

Tech Ops Engineer II - AMZ26688.4

Chicago, IL · On-site

$124K - $183K/yr

Amazon Data Services, Inc. Position: Tech Ops Engineer II - AMZ26688.4 Location: Chicago, IL Multiple Positions Available: 1. Utilize tools and templates for bid analysis and cost management. 2. ...

Tata Consultancy Service Limited

AI Ops Leader

Chicago, IL · On-site

$130K - $150K/yr

... with engineering, data, and platform teams Roles & Responsibilities • Define and implement AI Ops / MLOps / LLM Ops strategy for enterprise AI platforms • Manage end-to-end AI operations ...

Tata Consultancy Service Limited

AI Ops Leader

Chicago, IL · On-site

$130K - $150K/yr

... with engineering, data, and platform teams Roles & Responsibilities • Define and implement AI Ops / MLOps / LLM Ops strategy for enterprise AI platforms • Manage end-to-end AI operations ...

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All JobsData Ops Engineer JobsData Ops Engineer Jobs in Illinois

Data Ops Engineer information

See Illinois salary details

$43.1K

$125.7K

$172K

How much do data ops engineer jobs pay per year?

As of Jul 26, 2026, the average yearly pay for data ops engineer in Illinois is $125,698.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,000.00 and $133,200.00 per year, depending on experience, location, and employer.

What engineer makes 500,000 a year?

A Data Ops Engineer can earn $500,000 annually, especially at senior levels or in high-demand industries, often with extensive experience, advanced skills in automation, cloud platforms, and data management tools. Such compensation typically includes base salary, bonuses, and stock options, and is more common in large tech companies or executive roles.

What engineers make $300,000 a year?

Senior Data Ops Engineers with extensive experience, advanced skills in cloud platforms, automation, and data pipeline management can earn $300,000 or more annually. High compensation is often associated with roles in large organizations, specialized expertise, and leadership responsibilities.

What are Data Ops Engineers?

Data Ops Engineers are professionals who bridge the gap between data engineering and operations. They focus on automating, monitoring, and optimizing data pipelines to ensure reliable, efficient, and secure data flow within organizations. Their responsibilities often include managing data integration, workflow orchestration, deployment of data infrastructure, and implementing best practices for data quality and governance. Data Ops Engineers work closely with data scientists, analysts, and IT teams to support data-driven decision-making and maintain high data availability. Their role is crucial in modern organizations that rely on large-scale data processing and analytics.

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

To thrive as a Data Ops Engineer, you need a solid background in data engineering, automation, and cloud infrastructure, often supported by a degree in computer science or related field. Experience with tools like Apache Airflow, Docker, Kubernetes, CI/CD pipelines, and proficiency in scripting languages such as Python or Bash is typically required. Strong problem-solving skills, attention to detail, and effective communication help you collaborate with data teams and troubleshoot complex data workflows. These skills ensure reliable data delivery, streamlined operations, and scalable solutions that support organizational data goals.

What is the salary of DataOps specialist?

The salary of a DataOps specialist typically ranges from $80,000 to $130,000 annually, depending on experience, location, and industry. Professionals with skills in cloud platforms, automation tools, and scripting tend to earn higher salaries.

What does a DataOps engineer do?

A DataOps engineer is responsible for managing and automating data pipelines, ensuring data quality, and optimizing data workflows for faster and reliable data delivery. They often work with tools like Apache Spark, Kubernetes, and CI/CD systems, and require skills in scripting, cloud platforms, and data management practices to support data analytics and machine learning initiatives.

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

AspectData Ops EngineerData Engineer
CredentialsCertifications in data management, cloud platforms, scriptingCertifications in data engineering, SQL, cloud services
Work EnvironmentFocus on data pipelines, automation, deployment, and monitoringFocus on data modeling, ETL processes, database design
Industry UsageUsed in organizations emphasizing data operations, automation, and DevOps practicesUsed in data-centric roles focusing on building data infrastructure

While both roles work with data infrastructure, Data Ops Engineers primarily focus on automating and managing data pipelines and deployment processes, whereas Data Engineers concentrate on designing and building data systems. The roles often overlap but differ in their core focus areas and responsibilities.

How does a Data Ops Engineer typically collaborate with data scientists and software engineers within an organization?

Data Ops Engineers play a crucial role in bridging the gap between data science and engineering teams. They ensure smooth data pipeline operations, help automate workflows, and support data scientists by providing reliable, scalable infrastructure. Collaboration often involves participating in cross-functional meetings to understand data requirements, troubleshooting data quality issues, and implementing solutions that enable efficient experimentation and model deployment. This collaborative environment helps facilitate quick iterations and reliable delivery of data products.
What are popular job titles related to Data Ops Engineer jobs in Illinois? For Data Ops Engineer jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Data Ops Engineer jobs in Illinois look for? The top searched job categories for Data Ops Engineer jobs in Illinois are:
Data Ops Engineer jobs near you
Infographic showing various Data Ops Engineer job openings in Illinois as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $125,698 per year, or $60.4 per hour.
Data Analyst, Data Ops

Data Analyst, Data Ops

Harris Associates

Chicago, IL

Other

Posted 16 days ago

Job description

The Data Ops team is responsible for delivering scalable data, reporting and operational analytics solutions that support decision-making across the firm. The team partners closely with stakeholders to enable trusted, well governed data that improves transparency, efficiency, and outcomes.

The Data Analyst, Data Ops is responsible for ensuring the accurate, timely and reliable delivery of data and analytics solutions to support process efficiency and data-centric decision making. This role partners cross functionally to design, build and enhance data, reporting and workflow automation solutions. The Data Analyst supports enterprise analytics, BI, and self-service initiatives by ensuring data is consistently modeled, documented, governed and aligned with operational risk, audit, and regulatory expectations. Reporting to the Senior Director of Business Process and Data Ops, the ideal candidate combines strong analytical and problem-solving skills with an operational mindset, brings a solid understanding of investment management data domains and thrives in a collaborative, agile environment.

Responsibilities:

  • Partner with data consumers to understand data needs and ensure accuracy, reliability, and scalability of data and reporting outputs 
  • Design, develop, and maintain sophisticated data, reporting, and workflow automation solutions
  • Execute high-impact, cross-functional data initiatives from requirements through delivery
  • Gain deep expertise in firm-wide business processes and translate operational needs into scalable solutions
  • Lead BI, reporting, and self-service analytics initiatives by ensuring consistent, well-modeled, and well-documented dataservice analytics initiatives by ensuring consistent, wellmodeled, and welldocumented data
  • Champion enterprise data governance by upholding and evolving data standards, definitions, and stewardship practices
  • Contribute to the design and documentation of standard operating procedures, data controls, and issue management processes
  • Ensure data processes align with operational risk, audit, and regulatory expectations
  • Participate in firm-wide initiatives such as system implementations and data integrations
  • Drive agile planning, backlog refinement, and prioritization of Data Ops initiatives
  • Foster strong cross-functional relationships and promote a culture of collaboration, accountability and continuous improvement 

Qualifications:

  • Bachelor's degree in Data Analytics, Engineering, Computer Science, Business, or a related field
  • 5+ years of experience in investment management, financial services, or a data-intensive operations environment
  • Experience with cloud platforms (Snowflake, Azure), data quality tools, data catalogs, and BI platforms (PowerBI, Tableau)
  • Advanced proficiency with SQL and Python
  • Experience working with large datasets and using analytical tools to summarize, interpret, and deliver business value
  • Demonstrated ability to analyze data issues, identify root causes, and drive crossfunctional resolution
  • Exceptional attention to detail, initiative, and adaptability, with a proactive approach to identifying data anomalies, process gaps, and improvement opportunities
  • Strong communication and presentation skills, with the ability to explain complex data concepts to non-technical stakeholders and present findings and recommendations to senior leadership
  • Demonstrated proficiency with Agile methodology and project management practices
  • Experience with data engineering, pipeline development, JIRA, SharePoint, or workflow automation tools is a plus

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