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Data Operations Engineer Jobs (NOW HIRING)

Specter

Data Operations Engineer

San Francisco, CA · On-site

$81K - $110K/yr

Specter is hiring a data operations engineer to build our research data operation. This individual will own the full pipeline from defining what data we need, to getting it labeled at high quality ...

HappyRobot

Data Operations Engineer

San Francisco, CA · On-site

$81K - $110K/yr

The Data Operations Engineer will serve as the operational backbone of the product organization, working closely with the CTO and Head of Product to ensure synchronization between Product Engineering ...

Abaka AI

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

They are seeking a Data Operations Engineer to own and operate the internal dataset library, ensuring fast, accurate, and scalable access to data while coordinating with engineering, product, and ...

Abaka AI

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

The Data Operations Engineer will manage the internal dataset library and collaborate with various teams to ensure efficient access to data and maintain dataset quality. Responsibilities : • ...

Purpose Financial

Data Operations Engineer

Greenville, SC · On-site

$107K - $129K/yr

Position Summary We're looking for a skilled Data Operations Engineer to join our growing data team. This position reports to the Manager of Data Operations and is responsible for ensuring the ...

Abaka AI

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

The Data Operations Engineer will own and operate the internal dataset library, ensuring fast and scalable access to data while maintaining dataset quality and organization across the company.

Icario

$119K - $143K/yr

We are seeking a Data Operations Engineer to join our Data Engineering team under our Technology department. Using your creative problem solving skills, methodical attention to detail, and ...

Datasite

Data Operations Engineer

Minneapolis, MN · On-site

$119K - $143K/yr

Job Summary : Datasite is a company focused on data operations, and they are seeking a Data Operations Engineer to manage the full lifecycle of partner data. The role involves guiding data ...

HappyRobot Inc

Data Operations Engineer

San Francisco, CA · On-site

$150K - $200K/yr

You possess a rare mix of technical literacy (you understand APIs, data structures, and engineering constraints) and operational rigor. You don't just identify broken processes; you fix them. * A ...

MAQ Software

Software Data Operations Engineer

Plano, TX · On-site

$107K - $128K/yr

Software Data Operations Engineer Plano (Dallas), Texas About MAQ Software MAQ Software enables leading companies to accelerate their business intelligence and analytics initiatives. Our AI-powered ...

Huron

Data Operations Engineer Associate

$117K - $140K/yr

The Data Operations Engineer Associate is responsible for building and maintaining data pipelines, ensuring data quality, and supporting analytics solutions for healthcare organizations.

MAQ Software

Software Data Operations Engineer

Plano, TX · On-site

$107K - $128K/yr

Use tools such as GitHub Copilot to assist with query optimization and Azure DevOps MCP Server to validate data lineage and dependencies within project repositories. * Design processing steps and ...

MAQ Software

Software Data Operations Engineer

Redmond, WA · On-site

$85K - $120K/yr

Software Data Operations Engineer Redmond, Washington About MAQ Software MAQ Software enables leading companies to accelerate their business intelligence and analytics initiatives. Our AI-powered ...

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Data Operations Engineer information

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How much do data operations engineer jobs pay per year?

As of Jul 17, 2026, the average yearly pay for data operations engineer in the United States is $85,029.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,500.00 and $94,000.00 per year, depending on experience, location, and employer.

What does a data operations engineer do?

A data operations engineer manages and maintains data pipelines, ensuring data is collected, processed, and stored efficiently and accurately. They often work with tools like SQL, Python, and cloud platforms to automate data workflows, troubleshoot issues, and optimize data systems for analytics and reporting.

What is the difference between Data Operations Engineer vs Data Analyst?

AspectData Operations EngineerData Analyst
Required CredentialsBachelor's in CS, Data Science, or related; certifications like AWS, AzureBachelor's in Statistics, Math, or related; certifications like Microsoft Data Analyst
Work EnvironmentData engineering teams, cloud platforms, data pipelinesBusiness units, reporting tools, data visualization platforms
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing firms, finance, consulting, retail
Common Search & Comparison IntentUnderstanding technical differences, job roles, skillsData analysis tasks, reporting, insights generation

The Data Operations Engineer focuses on building and maintaining data infrastructure, pipelines, and ensuring data quality, often working with cloud platforms and scripting. In contrast, a Data Analyst primarily interprets data, creates reports, and provides insights to support business decisions. While both roles work with data, their core responsibilities and skill sets differ significantly.

What engineers make $300,000 a year?

Senior data operations engineers, especially those with extensive experience, advanced skills in data management, automation, and cloud platforms, can earn $300,000 or more annually. High compensation often depends on factors such as location, industry, certifications, and the complexity of data systems managed.

Can I make 200K as a data engineer?

Data engineers can earn $200,000 or more annually, especially with experience, advanced skills in cloud platforms, big data tools, and certifications. Salaries vary by location, industry, and company size, with senior roles and specialized expertise commanding higher pay.

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

To thrive as a Data Operations Engineer, you need a solid understanding of data management, ETL processes, and database systems, typically supported by a degree in computer science or a related field. Familiarity with tools like SQL, Python, data warehousing platforms, and cloud services (AWS, Azure, or GCP) as well as certifications such as AWS Certified Data Analytics are often required. Strong problem-solving, attention to detail, and effective communication skills help you manage complex data workflows and collaborate with cross-functional teams. These skills ensure data integrity, optimize performance, and enable seamless data-driven decision-making across the organization.

What engineers make $500,000?

Senior data operations engineers with extensive experience, advanced skills in data management, automation, and cloud platforms can earn $500,000 or more annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires specialized certifications, leadership roles, and a strong track record of managing complex data systems.

What are some common challenges faced by Data Operations Engineers when working with large-scale data pipelines?

Data Operations Engineers often encounter challenges such as maintaining data quality, ensuring pipeline reliability, and managing system scalability as data volumes grow. Troubleshooting failures in real-time data flows and coordinating with data engineering and analytics teams to address bottlenecks are also common tasks. Additionally, adapting to evolving technologies and implementing automation for routine maintenance can be demanding but are crucial for efficient operations.
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Data Operations Engineer jobs near you
Infographic showing various Data Operations Engineer job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $85,029 per year, or $40.9 per hour.
Data Operations Engineer

Data Operations Engineer

Specter

San Francisco, CA • On-site

$81K - $110K/yr

Full-time

Posted 14 days ago

Job description

Company Background:
Specter's mission is to help automate the physical world.
Today, we build video sensors with state-of-the-art AI agents that answer any question, anywhere in their environments. Our systems can automatically detect and reason about any physical activity captured on camera, from security incidents (e.g. perimeter intrusion, theft, LPR), to safety monitoring (e.g. PPE detection, injured people), to operational efficiency (e.g. material tracking, congestion monitoring). We offer both long range wireless (1km range) and wired sensor variants to suit any deployment.
Our co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.
Role:
Specter is hiring a data operations engineer to build our research data operation. This individual will own the full pipeline from defining what data we need, to getting it labeled at high quality, to ensuring it meets the needs of our research team and ultimately improves our models. The role sits at the intersection of engineering and research, with a focus on building systems and tooling.
Responsibilities:
  • Own the end-to-end relationship with our data labeling provider, including task scoping, timeline management, and issue resolution
  • Build and maintain internal tooling for labelers, including annotation interfaces, task pipelines, and dataset browsers
  • Define and enforce quality control standards across all labeled data, implementing automated checks and audit workflows
  • Partner with researchers to translate perception model needs into data collection strategies, identifying gaps in coverage across object types, scenes, lighting conditions, and sensor modalities
  • Build dashboards and metrics to monitor dataset diversity, class balance, and domain coverage
  • Close the loop on the data flywheel: track how labeled data flows into training, surface failure modes, and drive iteration on the pipeline from collection through to model improvement
  • Evaluate and integrate new data sources
  • Define labeling taxonomies and annotation specifications

Qualifications:
  • 1-3+ years of experience in data operations, project management, or a technical coordination role, ideally supporting ML or engineering teams
  • Proficiency in Python and comfort building lightweight tools, scripts, and dashboards
  • Strong written and verbal communication skills, with experience managing external vendors or cross-functional stakeholders
  • Familiarity with ML workflows and how training data impacts model performance
  • Highly organized, with a track record of managing multiple concurrent workstreams
  • Self-directed and autonomous
  • Bonus: experience with computer vision data, annotation platforms, or labeling operations

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