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Data Ops Engineer Jobs in Florida (NOW HIRING)

Raft

ML Ops Engineer

Tampa, FL · On-site

Raft is a customer-obsessed small business focused on Distributed Data Systems and Complex Application Development, headquartered in McLean, VA. They are seeking an ML Ops Engineer to design, build ...

Paysafe

Director, Data Engineering

Jacksonville, FL · On-site

Engineering Excellence & Data Ops * Champion engineering rigor through Data Ops , automation, CI/CD for data workloads, metadata management, and observability. * Drive improvements in data quality ...

Cliftonlarsonallen

Lead ML Ops Engineer

Naples, FL · On-site

$96K - $127K/yr

Degree in computer science, data science, or related field preferred. Technical Competencies ... Leadershiplevel expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.

Cliftonlarsonallen

Lead ML Ops Engineer

Orlando, FL · On-site

$95K - $126K/yr

Degree in computer science, data science, or related field preferred. Technical Competencies ... Leadershiplevel expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.

GeniusRx

Senior Data Engineer

Boca Raton, FL · On-site

$100K - $136K/yr

Work together with stakeholders from marketing, pharmacy ops, engineering, and product to understand how the data works, where it comes from, and where it needs to go. * Interface with other ...

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State of Florida

OPS Program Manager

Tallahassee, FL · On-site

$35/hr

Four (4) years of professional experience in a science or engineering discipline such as ... Ability to analyze and interpret data. * Ability to monitor and inspect project's deliverables.

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All JobsData Ops Engineer JobsData Ops Engineer Jobs in Florida

Data Ops Engineer information

See Florida salary details

$33.3K

$96.9K

$132.6K

How much do data ops engineer jobs pay per year?

As of Jul 3, 2026, the average yearly pay for data ops engineer in Florida is $96,936.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,600.00 and $102,800.00 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 Florida? For Data Ops Engineer jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Data Ops Engineer jobs in Florida look for? The top searched job categories for Data Ops Engineer jobs in Florida are:
Data Ops Engineer jobs near you
Infographic showing various Data Ops Engineer job openings in Florida as of June 2026, with employment types broken down into 98% Full Time, 1% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $96,936 per year, or $46.6 per hour.
ML Ops Engineer

ML Ops Engineer

Raft

Tampa, FL • On-site

Full-time

Posted 5 days ago

Job description

Job Summary:
Raft is a customer-obsessed small business focused on Distributed Data Systems and Complex Application Development, headquartered in McLean, VA. They are seeking an ML Ops Engineer to design, build, and maintain the infrastructure and pipelines for Machine Learning model training and deployment, collaborating with a cross-functional data team.
Responsibilities:
• Collaborate with a cross-functional data team comprising AI/ML Engineers, DevSecOps engineers, Product Owners, Data Engineers, Data Analysts, and Data Scientists.
• Design, build, and maintain the infrastructure and pipelines that enable Machine Learning model training, deployment, and scaling.
• Manage distributed workloads across GPU-enabled Kubernetes clusters and ensure efficient resource orchestration between training and inference operations.
Qualifications:
Required:
• 3+ years of relevant hands-on experience
• Experience building and maintaining machine learning pipelines
• Strong Python skills for defining and maintaining ML pipelines
• Practical experience with PyTorch (TensorFlow experience acceptable)
• Airflow for job orchestration, particularly managing resources between training and inference workloads
• Strong Kubernetes experience including managing local clusters, running different flavors, and managing custom resource definitions
• Istio networking experience in Kubernetes environments
• Experience working with MinIO object storage
• Must have hands-on experience running GPU workloads on Kubernetes
• Fast learner, analytical thinker, creative, hands-on, strong communication skills
• Able to work both independently and as part of a team
• Excellent problem-solving skills and attention to detail
• Active TS with ability to obtain and maintain SCI
Preferred:
• CENTCOM or DoD experience
• Experience with time slicing GPUs on Kubernetes
• Exposure to computer vision and/or large imagery formats such as NITF
• Publications or GitHub repos showcasing your skills
• Experience with Docker and container orchestration best practices
Company:
A niche consulting organization focused on Cloud Native, DevSecOps, and Modern Application Development for mission focused enterprises Founded in 2018, the company is headquartered in Reston, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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