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Data Ops Engineer Jobs (NOW HIRING)

CX Data Labs

Scrum Master - Data focus

Dallas, TX · On-site

$51 - $68/hr

Work closely with data engineers, data scientists, data analysts, BI engineers, etc., aligning data ... ops, business intelligence, etc. * Bonus: Experience scaling Agile across multiple teams (SAFe ...

Scientific Games

Manager, Tech Ops Engineer

Columbus, OH · On-site

$12K/mo

We push game designs to the next level and are pioneers in data analytics and iLottery. Built on a ... The Manager, Tech Ops Engineering is responsible for the overall integrity, performance, and ...

Ross

Engineer I

Dublin, CA · On-site

$99K - $130K/yr

As a key member of the Data engineering team, will work on diverse data technologies such as Snowflake, dbt, data ops and others to build insightful, scalable, and robust data pipelines that feed our ...

AspireIT Solutions

Senior ML Ops Engineer

Dallas, TX · On-site

$103K - $142K/yr

Role Summary We are looking for a Senior ML Ops Engineer to own the full ML lifecycle -- from model ... Big data technologies: Apache Spark, Hadoop * Awareness of ethical ML concerns (e.g., selection ...

R2 Technologies

AZURE ML ops Engineer

Alpharetta, GA · On-site

$54.50 - $72.75/hr

Role: AZURE ML ops Engineer Location: Atlanta, GA #Role is on-site, Must relocate Must w2 Role ... Data science model review, run the code refactoring and optimization, containerization, deployment ...

Patterson-UTI

Data Ops Lead

Houston, TX · On-site

This role sits at the intersection of data engineering, platform operations, and reliability , supporting critical data pipelines that power analytics, reporting, and operational decision-making ...

West Advanced Technologies (WATI)

Dev Ops Engineer

Downey, CA · On-site

$54.50 - $74.75/hr

Dev Ops Engineer Downey, CA 12+ months Position Description: A DevOps Engineer serves as the ... Have a minimum of seven (7) years of experience in electronic data processing systems study, design ...

Patterson-UTI

Data Ops Lead

Houston, TX · On-site

This role sits at the intersection of data engineering, platform operations, and reliability , supporting critical data pipelines that power analytics, reporting, and operational decision-making ...

Patterson-UTI

Data Ops Lead

Houston, TX

This role sits at the intersection of data engineering, platform operations, and reliability , supporting critical data pipelines that power analytics, reporting, and operational decision-making ...

Ross Stores

Engineer I

Dublin, CA

$99K - $130K/yr

As a key member of the Data engineering team, will work on diverse data technologies such as Snowflake, dbt, data ops and others to build insightful, scalable, and robust data pipelines that feed our ...

Logistics Management Institute

The ML Ops Engineer will work at the intersection of advanced AI/ML development, machine learning ... Design and implement advanced data manipulation and pipelining workflows using tools such as Pandas ...

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All JobsData Ops Engineer Jobs

Data Ops Engineer information

See salary details

$44.5K

$129.7K

$177.5K

How much do data ops engineer jobs pay per year?

As of Jul 3, 2026, the average yearly pay for data ops engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 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.
More about Data Ops Engineer jobs
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What job categories do people searching Data Ops Engineer jobs look for? The top searched job categories for Data Ops Engineer jobs are:
Data Ops Engineer jobs near you
Infographic showing various Data Ops Engineer job openings in the United States as of June 2026, with employment types broken down into 99% Full Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Scrum Master - Data focus

CX Data Labs

Dallas, TX • On-site

$51 - $68/hr

Full-time

Posted 2 hours ago

Job description

Scrum Master

Dallas, TX

Fulltime

Key Responsibilities

  • Lead & facilitate Scrum (or Agile) ceremonies for data and analytics teams: Sprint Planning, Daily Stand-ups, Reviews, Retrospectives.
  • Ensure product backlog for data pipelines, analytics features, data quality, and infrastructure is well groomed; provide support to Data Product Owners or Analytics Product Owners.
  • Work closely with data engineers, data scientists, data analysts, BI engineers, etc., aligning data deliverables with stakeholder requirements.
  • Identify, communicate, and remove impediments specifically in data workflows (e.g. data source issues, ETL/ELT blockers, dependencies, tooling constraints).
  • Coach teams in Agile best practices applicable to data work (incremental delivery of data assets, continuous validation, versioning of data schemas, etc.).
  • Manage dependencies between data teams, engineering, operations, and business stakeholders.
  • Maintain metrics around velocity, data quality, cycle time, throughput, etc., and use them to drive continuous improvement.
  • Assist with release planning where data deliverables align with product / BI / ML deliverables.
  • Promote good data practices: data lineage, documentation, testing, monitoring.

Qualifications & Skills

  • 5+ years of experience as a Scrum Master or Agile facilitator. Experience with data-oriented teams highly preferred.
  • Strong understanding of data workflows: ETL/ELT, data pipelines, data ingestion, transformation, analytics, BI, possibly ML teams.
  • Certifications: CSM, PSM, or SAFe Scrum Master or equivalent Agile credentials. Agile Coaching experience a plus.
  • Excellent communication, stakeholder management, conflict resolution. Ability to translate business/data/stakeholder needs.
  • Experience working with Agile tools (e.g., Jira, Confluence, Azure DevOps, or others), and familiarity with data tools / version control (where relevant) is a plus.
  • Ability to handle ambiguity, changing priorities; comfortable working with cross-functional data stakeholders, data ops, business intelligence, etc.
  • Bonus: Experience scaling Agile across multiple teams (SAFe, LeSS, or similar), experience with data governance / data quality / data ops.

Senior / Performance Expectations

  • Lead multiple data teams, or be responsible for complex data deliverables across teams.
  • Drive improvements in data delivery cycle times, quality, reliability.
  • Mentor junior Scrum Masters or Agile champions.
  • Influence process improvements not just within teams but at a department or organization level.