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Dataops Jobs (NOW HIRING)

Trioptus

DataOps Engineer

Montpelier, VT · On-site

DataOps Engineer Job Type: Contract Duration: 12-15 months (with potential for extension) Work Location: Remote (U.S.-based) Work Hours: Standard business hours Job Overview We are seeking an ...

TriOptus LLC

DataOps Engineer

Montpelier, VT · Remote

DataOps Engineer Job Type: Contract Duration: 12 15 months (with potential for extension) Work Location: Remote (U.S.-based) Work Hours: Standard business hours Job Overview We are seeking an ...

Trioptus

DataOps Engineer

Montpelier, VT · Remote

DataOps Engineer Job Type: Contract Duration: 12 15 months (with potential for extension) Work Location: Remote (U.S.-based) Work Hours: Standard business hours Job Overview We are seeking an ...

TriOptus LLC

DataOps Engineer

Montpelier, VT · On-site

$115.80K - $139K/yr

DataOps Engineer Job Type: Contract Duration: 12-15 months (with potential for extension) Work Location: Remote (U.S.-based) Work Hours: Standard business hours Job Overview We are seeking an ...

Lorven Technologies

DataOps & Build Engineer

DataOps & Build Engineer will lead the architecture and optimization of a next-generation data platform. This critical role requires expertise to drive technical direction, mentor teams, and automate ...

Panopto

DataOps Engineer

OR · Remote

$140K - $150K/yr

As we scale our global reach, we are seeking a DataOps Engineer to architect the 'Golden Path' for our data infrastructure. You will transform how we manage the data powering Panopto for 10+ million ...

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How much do dataops jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for dataops in the United States is $23.13, according to ZipRecruiter salary data. Most workers in this role earn between $17.55 and $24.04 per hour, depending on experience, location, and employer.

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

To thrive as a DataOps Engineer, you need expertise in data engineering, automation, cloud platforms, and a solid understanding of CI/CD pipelines, typically backed by a degree in computer science or related fields. Familiarity with tools like Apache Airflow, Kubernetes, Docker, Jenkins, and cloud services such as AWS, GCP, or Azure is commonly required, along with knowledge of scripting languages like Python or Bash. Strong collaboration, problem-solving, and communication skills help DataOps professionals work effectively across data, development, and operations teams. These abilities ensure reliable, scalable, and efficient data infrastructure, enabling organizations to quickly deliver high-quality data solutions.

How does a DataOps professional typically collaborate with data engineers, analysts, and other IT teams?

DataOps professionals play a key role in bridging the gap between data engineering, analytics, and IT by facilitating efficient, automated workflows and ensuring data quality across the pipeline. They often work closely with data engineers to streamline data integration and deployment processes, while collaborating with analysts to support timely access to reliable data. Regular communication and cross-functional teamwork are essential, as DataOps is responsible for implementing best practices that help different teams deliver insights faster and with fewer errors. This collaborative environment also encourages continuous feedback and process improvement.

What are DataOps?

DataOps, short for Data Operations, is a set of practices, processes, and technologies that combine data engineering, data integration, and DevOps methodologies to improve the quality and speed of data analytics. DataOps aims to streamline the flow of data from source to value, enabling organizations to deliver reliable, high-quality data to stakeholders more efficiently. This approach emphasizes collaboration, automation, and monitoring throughout the data lifecycle to reduce errors and shorten development cycles. The ultimate goal of DataOps is to create an agile data pipeline that adapts quickly to changing business needs.

What is the difference between Dataops vs Data Engineer?

AspectDataopsData Engineer
Primary FocusAutomating data workflows, deployment, and operational efficiencyBuilding and maintaining data pipelines, storage, and infrastructure
Skills & CertificationsDevOps tools, scripting, cloud platforms, CI/CD practicesSQL, ETL tools, cloud platforms, programming (Python, Scala)
Work EnvironmentCollaborates with DevOps, data teams, and operationsWorks closely with data scientists, analysts, and infrastructure teams
Industry UsageUsed in organizations focusing on data deployment and automationUsed in data infrastructure development and data pipeline creation

While both Dataops and Data Engineers work with data infrastructure, Dataops emphasizes automation, deployment, and operational efficiency, whereas Data Engineers focus on building and maintaining data pipelines and storage systems. Understanding these differences helps organizations assign the right roles for their data needs.

More about Dataops jobs
What cities are hiring for Dataops jobs? Cities with the most Dataops job openings:
What are the most commonly searched types of Dataops jobs? The most popular types of Dataops jobs are:
What states have the most Dataops jobs? States with the most job openings for Dataops jobs include:
What job categories do people searching Dataops jobs look for? The top searched job categories for Dataops jobs are:
Dataops jobs near you
Infographic showing various Dataops job openings in the United States as of May 2026, with employment types broken down into 87% Full Time, and 13% Contract. Highlights an 62% In-person, and 38% Remote job distribution, with an average salary of $48,110 per year, or $23.1 per hour.
DataOps Engineer

DataOps Engineer

Trioptus

Montpelier, VT • On-site

Contractor

Posted 3 days ago

Job description

Job Title: DataOps Engineer

Job Type: Contract

Duration: 12–15 months (with potential for extension)

Work Location: Remote (U.S.-based)

Work Hours: Standard business hours

Job Overview

We are seeking an experienced DataOps Engineer to support a large‑scale healthcare data modernization initiative. This role focuses on building and operationalizing modern data quality, governance, analytics, and performance monitoring capabilities within an enterprise data environment.

The ideal candidate will combine strong technical expertise with the ability to collaborate closely with internal teams through hands‑on delivery, documentation, and knowledge transfer. The engagement emphasizes long‑term sustainability, staff enablement, and repeatable best practices rather than one‑time development.

Key Responsibilities

DataOps & Data Quality

  • Develop and maintain data quality rules, validation frameworks, and monitoring processes
  • Implement statistical process control (SPC) and anomaly detection to ensure data reliability
  • Support incident logging, triage, root‑cause analysis, and continuous improvement efforts

Data Governance & Metadata

  • Define and maintain metadata standards, data glossary entries, and end‑to‑end data lineage
  • Establish governance procedures, SOPs, and disclosure/suppression rules
  • Translate downstream analytics and governance requirements into clear upstream data specifications

Analytics & Business Intelligence

  • Design and support governed semantic data models
  • Assist with the development and validation of standardized dashboards and reports
  • Ensure data accuracy, refresh reliability, and compliance with governance standards

Platforms & Tools

  • Work within platforms such as Azure DevOps, cloud‑based data lakes, and BI tools
  • Maintain operational dashboards, KPIs, and performance metrics
  • Support agile workflows, documentation repositories, and collaboration tools

Enablement & Collaboration

  • Partner with internal teams through hands‑on co‑development sessions
  • Create and maintain Wikis, runbooks, and playbooks for long‑term ownership
  • Deliver role‑based training and support organizational readiness initiatives

Required Qualifications

  • Proven experience as a DataOps Engineer, Data Engineer, Analytics Engineer, or similar role
  • Strong background in data quality engineering, governance, and analytics enablement
  • Experience with:
    • Azure DevOps or similar workflow tools
    • Business intelligence platforms (e.g., Power BI or equivalent)
    • Cloud data platforms (Azure, AWS, or similar)
  • Experience working in Agile or iterative delivery environments
  • Strong documentation, communication, and stakeholder collaboration skills
  • Ability to work independently in a remote environment

Preferred Qualifications

  • Certifications such as:
    • Azure Data Engineer
    • Power BI Data Analyst
    • Databricks Data Engineer
    • Data Governance or Data Management certifications
  • Experience in healthcare, public sector, or highly regulated data environments
  • Familiarity with change management and operational enablement practices
     

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