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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 ...

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 ...

Apple

ML Data Operations Engineer

Santa Clara, CA · On-site

Description Apple's ML Data Operations group is seeking a Data Operations Engineer to support internal data collection efforts powering our next generation of consumer machine learning features. In ...

FieldAI

Senior Data Operations Engineer

Boston, MA

$110K - $140K/yr

FAIRI is looking for a Senior Data Operations Engineer to run the day-to-day of our data operations function: building the tooling the pipeline runs on, staying hands-on enough to dogfood and stress ...

Apple

ML Data Operations Engineer

Cupertino, CA · On-site

Description Apple's ML Data Operations group is seeking a Data Operations Engineer to support internal data collection efforts powering our next generation of consumer machine learning features. In ...

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How much do data operations engineer jobs pay per year?

As of Sep 2, 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 are the key skills and qualifications needed to thrive as a data operations engineer?

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 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.

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 does a data operations engineer do?

A data operations engineer manages and maintains data pipelines, ensuring data is collected, processed, and stored efficiently for analysis. They often work with tools like SQL, Python, and cloud platforms to automate workflows, monitor data quality, and support data-driven decision-making.
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Data Operations Engineer jobs near you
Infographic showing various Data Operations Engineer job openings in the United States as of August 2026, with employment types broken down into 84% Full Time, 13% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $85,029 per year, or $40.9 per hour.

Data Operations Engineer

Specter

San Francisco, CA • On-site

$81K - $110K/yr

Full-time

Re-posted yesterday

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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