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Remote Data Tagging Jobs in Redmond, WA (NOW HIRING)

All JobsData Tagging JobsRemote Data Tagging JobsRemote Data Tagging Jobs in Redmond, WA

Remote Data Tagging information

See Redmond, WA salary details

$61.6K

$111.1K

$151.8K

How much do remote data tagging jobs pay per year?

As of Aug 8, 2026, the average yearly pay for remote data tagging in Redmond, WA is $111,134.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,300.00 and $121,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a remote data tagging specialist?

To thrive as a Remote Data Tagging Specialist, you need strong attention to detail, basic data analysis skills, and familiarity with data labeling concepts, typically supported by a high school diploma or higher. Experience with data annotation tools such as Labelbox, Supervisely, or CVAT, and understanding of formats like JSON or XML, is commonly required. Excellent time management, communication, and reliability are standout soft skills for this remote role. These abilities are crucial to ensure high-quality, accurate datasets that power machine learning and AI systems.

What are some common challenges faced by remote data tagging professionals and how can they be addressed?

Remote data tagging professionals often encounter challenges such as maintaining consistent accuracy, managing repetitive tasks, and staying engaged without in-person supervision. To address these, it’s helpful to follow clear annotation guidelines, take regular breaks to prevent fatigue, and use collaboration tools to communicate with team members or supervisors. Many organizations also provide feedback loops and quality checks to support remote taggers in improving their work and staying aligned with project expectations.

What is the difference between Remote Data Tagging vs Remote Data Annotation?

AspectRemote Data TaggingRemote Data Annotation
Primary FocusLabeling specific data points within datasetsAdding detailed labels and context to data
Skills RequiredAttention to detail, basic understanding of data typesAnalytical skills, understanding of data context
Work EnvironmentRemote, often part-time or freelanceRemote, often part-time or freelance
Industry UsageMachine learning, AI trainingMachine learning, AI training

Remote Data Tagging and Remote Data Annotation are closely related tasks in AI data preparation. Tagging typically involves marking specific data points, while annotation provides more detailed context. Both roles are essential for training machine learning models and share similar skills and work environments.

What is remote data tagging?

Remote data tagging is the process of labeling or annotating data—such as images, videos, text, or audio—while working from a remote location, typically from home. This work is essential for training artificial intelligence (AI) and machine learning models, as it helps computers better understand and categorize information. Data taggers use specialized software to mark, classify, or add metadata to various types of digital content according to specific guidelines provided by employers or clients. Most remote data tagging jobs require attention to detail, basic computer skills, and the ability to follow detailed instructions. These roles are often offered as freelance, part-time, or full-time remote positions.
What job categories do people searching Remote Data Tagging jobs in Redmond, WA look for? The top searched job categories for Remote Data Tagging jobs in Redmond, WA are:
What cities near Redmond, WA are hiring for Remote Data Tagging jobs? Cities near Redmond, WA with the most Remote Data Tagging job openings:
Remote Data Tagging jobs near you
Infographic showing various Remote Data Tagging job openings in Redmond, WA as of June 2026, with employment types broken down into 82% Full Time, 5% Part Time, 3% Temporary, and 10% Contract. Highlights an 100% Remote job distribution, with an average salary of $111,134 per year, or $53.4 per hour.

Azure Data Engineer

Meta Force Technology Staffing LLC

Seattle, WA • Remote

$61.50 - $79.75/hr

Contractor

Re-posted 6 days ago

Job description

Azure Data Engineer Roles - 100% Remote Role

Required Skills & Experience

  • 8+ years in release engineering & build automation (strong Azure expertise required)
  • Hands-on with Git, Azure DevOps Pipelines, and GitHub Actions
  • Proficient in Git workflows: branching, PR reviews, validation gates, branch protections
  • Deep understanding of Azure services:
    • VNets, NSGs, Virtual Machines
    • Azure Storage, Managed Identities, Key Vault
    • Load Balancer, Application Gateway, AKS fundamentals
  • Experience building reusable release templates and task libraries in YAML-based pipelines and GitHub Actions
  • Skilled in Python and PowerShell scripting for automation and tooling
  • Experience with ADF, Databricks, or Snowflake code promotions across environments
  • Knowledge of rollback and recovery patterns including pre-deployment backups and hotfix flows
  • Familiarity with containerized release tasks and secret management via Key Vault or Managed Identities
  • Understanding of Docker, Azure Container Registry, and container-based promotions
  • Strong focus on data release:
    • Promoting data pipelines
    • Monitoring releases
    • Tagging, cost tracking, operational reporting
  • Ability to build a 24/7 release framework:
    • Clear SLAs
    • Monitoring
    • Fast recovery with rollback playbooks
  • Effective stakeholder collaboration across Data Engineering, Cloud Architecture, Security, and Business Operations

Plusses

  • Azure Certifications: AZ-900, AZ-104, AZ-400
  • Bachelor's degree in Computer Science, Engineering, or related field
  • Strong grasp of artifact versioning, publishing practices, and dependency management
  • Experience building dashboards and observability tools for deployment metrics and release health
  • Exposure to advanced deployment strategies:
    • Parallel, Queued, Blue-Green, Canary Releases
  • Knowledge of FinOps practices: tagging, right-sizing, cloud cost optimization