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Remote Dataops Engineer Jobs (NOW HIRING)

FICO

Lead ML Ops/DevOps Engineer - AI Engineering

$140K - $220K/yr

What We're Seeking * 8+ years of experience in DataOps, MLOps, or related fields, with 3+ years ... LS1 #LI-Remote Why Make a Move to FICO? At FICO, you can develop your career with a leading ...

CrowdStrike

Establish best practices for MLOps/DataOps surrounding LLMs, including monitoring, observability ... Actively mentor engineers, conducting technical workshops, leading design reviews, and ...

Huntington

MLOps Automation Senior Lead Engineer

Austin, TX · On-site +1

$103K - $135K/yr

Experience with agile development and strong understanding of DataOps and ModelOps principles ... Remote roles will also have the opportunity to come together in our offices for moments that matter.

Huntington

MLOps Automation Senior Lead Engineer

Columbus, OH · On-site +1

$100K - $131K/yr

Experience with agile development and strong understanding of DataOps and ModelOps principles ... Remote roles will also have the opportunity to come together in our offices for moments that matter.

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Remote Dataops Engineer information

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$38K

$115.9K

$191.5K

How much do remote dataops engineer jobs pay per year?

As of Aug 29, 2026, the average yearly pay for remote dataops engineer in the United States is $115,864.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,000.00 and $151,500.00 per year, depending on experience, location, and employer.

What is a remote DataOps engineer?

A Remote DataOps Engineer is a technology professional who works remotely to manage, automate, and optimize data pipelines and processes within an organization. Their main goal is to streamline the flow of data between various systems, ensuring the availability, quality, and security of data for analytics and business operations. They collaborate closely with data engineers, analysts, and IT teams to develop efficient workflows, automate repetitive tasks, and monitor data infrastructure performance. By working remotely, they leverage cloud-based tools and communication platforms to support distributed teams and global data operations.

How do remote DataOps engineers typically collaborate with cross-functional teams to ensure smooth data pipeline operations?

Remote DataOps Engineers often work closely with data engineers, analysts, and DevOps teams using collaboration tools like Slack, Jira, and GitHub. Regular virtual meetings and documentation are essential to align on pipeline requirements, monitor data quality, and resolve issues quickly. Clear communication and proactive status updates help build trust with stakeholders and ensure that data infrastructure supports business needs efficiently, even when working remotely.

What are the key skills and qualifications needed to thrive as a remote DataOps engineer, and why are they important?

To thrive as a Remote DataOps Engineer, you need expertise in data engineering, automation, and pipeline management, typically backed by a degree in computer science or a related field. Familiarity with tools like Apache Airflow, Kubernetes, CI/CD systems, and cloud platforms such as AWS or Azure is highly valued, along with relevant certifications. Excellent problem-solving, communication, and collaboration skills are crucial for managing distributed teams and complex data workflows. These skills and qualities ensure efficient, reliable, and scalable data infrastructure essential for modern data-driven organizations.

What is the difference between Remote Dataops Engineer vs Data Engineer?

AspectRemote Dataops EngineerData Engineer
CredentialsBS in CS, Data Science, or related; certifications like AWS, GCP, or AzureSimilar credentials; often with additional database or software engineering certifications
Work EnvironmentRemote or hybrid, collaborative teams, cloud platformsRemote or on-site, data-focused teams, cloud and on-prem infrastructure
Industry UsageTech, finance, healthcare, e-commerceTech, finance, retail, telecom
Search & Comparison IntentUnderstanding roles in data operations, cloud deployment, automationFocus on data pipeline development, database management, ETL processes

The Remote Dataops Engineer and Data Engineer roles share many credentials and work environments, often overlapping in cloud and data infrastructure. However, Data Engineers typically focus more on building and maintaining data pipelines and databases, while Dataops Engineers emphasize automating deployment, monitoring, and optimizing data workflows in cloud environments.

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Remote Dataops Engineer jobs near you
Infographic showing various Remote Dataops Engineer job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $115,864 per year, or $55.7 per hour.

Customer Reliability Engineer - Airflow

Boston, NY • On-site, Remote

Astronomer
Software Development • 201 - 500 employees
Learn more about Astronomerexternal

$125K - $130K/yr

Full-time

Re-posted 6 days ago

Job description

Astronomer empowers data teams to bring mission-critical software, analytics, and AI to life and is the company behind Astro, the industry-leading unified DataOps platform powered by Apache Airflow®. Astro accelerates building reliable data products that unlock insights, unleash AI value, and powers data-driven applications. Trusted by more than 800 of the world's leading enterprises, Astronomer lets businesses do more with their data. To learn more, visit www.astronomer.io.
About this role:
As an Airflow Reliability Engineer on the Customer Reliability Engineering (CRE) team at Astronomer, you will have the opportunity to become an Apache Airflow expert, learning directly from leaders of the Airflow project. You'll provide Apache Airflow expertise directly to customers to help them make the best possible use of our managed Airflow service.
CRE is Astronomer's support team. Because our customers are sophisticated organizations who need and expect high levels of expertise to help them keep mission critical uses of Apache Airflow working consistently, we look a little different from most support teams. Nearly every ticket you will work requires an intersection of strong technical knowledge and customer empathy to understand what the customer needs and how to get them there. Every day is a new challenge and a new thing to learn.
When you learn a new piece of technology, are you aiming not just to get started but to become the expert? Do you listen to the plumber when they tell you what is wrong with the pipes? Are you the kind of person who takes an MIT OpenCourseWare course and actually finishes it? Then this role could be for you.
What you get to do:
  • Learn and build expertise across several software engineering disciplines, including:
  • Airflow and data engineering
  • Kubernetes
  • Cloud Engineering
  • Gain exposure to the big picture; learn about product, engineering, customer relationship management, and more.
  • Solve challenging Airflow problems for our customers. From optimizing configuration to identifying world-first Airflow bugs, you'll see it all here.
  • Spend up to 20% of your time on side projects that contribute to Astronomer's overall success, such as contributing to the open-source Airflow repository or developing Astronomer's internal monitoring and alerting systems built on Airflow.
  • Work on a modern, sophisticated, cloud-native product that customers use to connect to dozens of other systems. Gain depth and breadth of learning!
  • Work directly with our customers' data engineers, system admins, DevOps teams, and management.
  • Provide feedback from your experience that can shape the direction of the Airflow project.
  • Own the customer experience, working directly with customers to prioritize and solve issues, meet SLAs, and provide "white glove" guidance on the path to production.
  • Participate remotely within a fully distributed team.
  • Help maintain 24x7 coverage through a specified 6-hour pager period during your work day.
  • Participate in paid on-call rotation for weekend coverage.

What you bring to the role:
  • Data Engineering background
  • 4 years of experience with Python
  • 1 year of experience in Airflow administration and DAG creation
  • Experience with Kubernetes/Docker/Containers
  • Experience working with a distributed system with any major cloud provider (AWS, GCP, Azure)
  • Problem-solving and troubleshooting abilities
  • Ability to work well with autonomy and independence
  • Strong written and verbal communication for connecting with our customers over our ticketing system and through Zoom
  • Experience mentoring junior team members

Bonus points if you have:
  • Contributions to open-source projects
  • Customer Support experience
  • Familiarity with SQL and PostgreSQL
  • Experience with Databricks, Snowflake, Redshift, dbt, or other similar data engineering tools

The estimated total compensation for this role ranges from $125,000 - $130,000 based on leveling and geography, along with an equity component and a comprehensive benefits package. This range is merely an estimate; actual compensation may deviate from this range based on skills, experience, and qualifications.
#LI-Fulltime
#LI-Remote
At Astronomer, we value diversity. We are an equal opportunity employer: we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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