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Data Ops Engineer Jobs (NOW HIRING)

Neonmoneytalks

Data Ops Lead

Manhattan, NY · On-site

As a Data Ops Lead, you'll own the end-to-end journey that takes raw recordings from our growing ... engineering and research teams at frontier labs to translate their exact specifications into ...

Donato Technologies, Inc

ML Ops Engineer

Charlotte, NC · On-site

$60/hr

Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks. * Solid understanding of software engineering principles and DevOps practices. * Ability to communicate complex ...

Neon Mobile, Inc

Data Ops Lead

New York, NY · On-site +1

$160K - $220K/yr

As a Data Ops Lead, you'll own the end-to-end journey that takes raw recordings from our growing ... engineering and research teams at frontier labs to translate their exact specifications into ...

Wise Skulls

Platform Ops Engineer

Title: Platform Ops Engineer Location: Any Infosys hub location (Hybrid) Duration: 6 months ... Perform Foundry platform monitoring using pipeline health, data freshness, and ontology integrity ...

Epicor

Senior Data Ops Developer

Lehi, UT · Hybrid

Step into your future as a DataOps Developer and help shape a growing enterprise data platform. In this role, you will design and maintain cloud-based data pipelines, automate data processes, and ...

Epicor

Senior Data Ops Developer

Dublin, GA · Hybrid

Step into your future as a DataOps Developer and help shape a growing enterprise data platform. In this role, you will design and maintain cloud-based data pipelines, automate data processes, and ...

Epicor

Senior Data Ops Developer

Austin, TX · Hybrid

Step into your future as a DataOps Developer and help shape a growing enterprise data platform. In this role, you will design and maintain cloud-based data pipelines, automate data processes, and ...

Boston Childrens Hospital

$93K - $149K/yr

The ML Ops Engineer II works in close collaboration with data scientists and various stakeholders across the hospital to develop solutions that improve patient care outcomes and operational ...

Saransh Inc

AI Ops Engineer

Reston, VA · On-site

$72K - $97K/yr

AI Ops Engineer Location: Reston, VA (3 to 4 days onsite is must) Job Type: Contract Required ... Solid experience integrating AI agents with various data sources, including Elasticsearch, SQL and ...

Techvilla Solutions

ML Ops Engineer

Brookfield, WI · On-site

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Techvilla Solutions

ML Ops Engineer

Menomonee Falls, WI · On-site

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Techvilla Solutions

ML Ops Engineer

Greenfield, WI · On-site

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Scientific Games

Manager, Tech Ops Engineer

Columbus, OH · On-site

$125K/yr

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 ...

Mimecast

Senior ML Ops Engineer

Columbus, OH · On-site

$100K - $138K/yr

Senior ML Ops Engineer Overview As a Senior ML Ops Engineer at Mimecast, you will be a technical ... Continuously monitor model performance, data drift, latency, error rates, and system health. Build ...

Mimecast

Senior ML Ops Engineer

Columbus, OH · On-site

$100K - $138K/yr

Senior ML Ops Engineer Overview As a Senior ML Ops Engineer at Mimecast, you will be a technical ... Continuously monitor model performance, data drift, latency, error rates, and system health. Build ...

Techvilla Solutions

ML Ops Engineer

Greendale, WI · On-site

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Techvilla Solutions

ML Ops Engineer

Fox Point, WI · On-site

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Techvilla Solutions

ML Ops Engineer

Muskego, WI · On-site

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

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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 Sep 10, 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 is a Data Ops Engineer?

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.

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 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 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.

Is data operations a good career?

Data Operations, often involving roles like Data Ops Engineer, is a growing field focused on managing data pipelines, automation, and infrastructure. It offers strong job demand, competitive salaries, and opportunities to work with tools like cloud platforms and data management systems, making it a viable career choice for those interested in data and technology.
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What are popular job titles related to Data Ops Engineer jobs?

For Data Ops Engineer jobs, the most frequently searched job titles are:

Data OPS Engineer jobs near you
Infographic showing various Data Ops Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Data Ops Lead

Manhattan, NY • On-site

Neonmoneytalks
See more jobs at Neonmoneytalksexternal

Other

Posted 9 days ago

Key responsibilities

  • Own the end-to-end process of transforming raw consumer audio streams into production-ready datasets for AI labs.

  • Manage data deals, ensure dataset quality, and oversee human transcription, annotation, and QA operations, largely overseas.

  • Collaborate with internal teams and external vendors to support data pipeline development and meet buyer specifications.

Job description

About Neon

Many large companies make billions each year by monetizing Americans’ personal data. At Neon, we’re finally cutting consumers in on the deal. Neon allows our users to make hundreds (or even thousands) of dollars per year by securely selling their anonymized data. We’re backed by Lightspeed, Upper90, Upfront Ventures, and other cool investors.

About the role

Your mission is to turn Neon's raw consumer audio streams into the cleanest, most reliable training data on the market, and to build the commercial and operational engine that gets it into the hands of the world's leading AI labs.

As a Data Ops Lead, you'll own the end-to-end journey that takes raw recordings from our growing community of 500,000+ mobile users and delivers production-ready datasets to frontier labs. In practice, that means three things above all:

  • Structuring and managing the data deals that turn our recordings into revenue
  • Holding every dataset to a quality bar that keeps buyers coming back
  • Standing up human transcription, annotation and other operations, largely overseas, that make it all possible

You’ll work directly with our CEO on commercial priorities and help shape each deal, interface with buyer-side engineering and research teams at frontier labs to translate their exact specifications into deliverable dataset plans, and partner with internal engineering and external vendors to make sure the pipeline supports what we’ve sold. This is a foundational role: the datasets and processes you build are the product we sell.

You have…
  • Authorization to work in the US.
  • 5+ years of experience building and scaling data pipelines for AI/ML applications, with significant time spent on audio, speech, or multimodal data.
  • A track record of structuring and delivering against data or dataset agreements with external partners: taking their requirements, turning them into clear specifications, and owning delivery end to end.
  • Experience building and managing overseas or outsourced teams for data tagging, annotation, and QA, with a track record of maintaining quality and throughput across time zones.
  • Deep ownership of data quality: designing QA processes, defining acceptance criteria, and catching problems before a customer ever sees them.
  • Enough technical fluency to be credible on both sides of a deal. You understand digital audio fundamentals (sample rates, VAD, multichannel formats), can reason about how pipelines are built, and know what "good" looks like, even if you’re not writing every line of code yourself.
  • A "Founder's Mentality." You’re comfortable building from zero and making high-stakes calls with incomplete information.
Bonus points
  • A background working with audio data in some capacity.
  • Direct experience with training data for TTS, ASR, speaker ID, or full-duplex conversational models.
  • Familiarity with the modern audio stack (Librosa, FFmpeg, SoX, torchaudio) and cloud data infrastructure (S3, Redshift, BigQuery, or equivalent).
  • An understanding of how high-quality, speaker-separated audio gets captured (for example, via WebRTC-based recording tools).
  • Experience with active learning loops, human-in-the-loop QA systems, or corpus stratification for balanced dataset design.
  • Prior experience leading a data or infrastructure team, including hiring and mentoring engineers.
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