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Data Annotation Tech Remote Jobs in Michigan (NOW HIRING)

Director of Data Intelligence | Remote | Michigan or Minnesota Preferred Role Snapshot: * Set the ... Establish and maintain relationships with key technology providers, vendors, and industry ...

This role will partner with Commercial, Sales Operations, Digital/IT, Data, Legal, Compliance, and ... REMOTE. * Pay Rate: $70/hr. on C2C. or $65/hr. on W2. * Interviews: Video interviews. * Docs ...

Whether you've got deep experience in commercial real estate, skilled trades or technology, or you ... Location: Remote -Chicago, IL, Cleveland, OH, Columbus, OH, Detroit, MI, Indianapolis, IN ...

Remote micro1 is engaging Business Document Experts (Excel, PowerPoint, Word) to participate in a ... consulting, tech, retail, or related industries. * Extensive proficiency with advanced Excel ...

Remote micro1 is engaging Business Document Experts (Excel, PowerPoint, Word) to participate in a ... consulting, tech, retail, or related industries. * Extensive proficiency with advanced Excel ...

Showing results 41-60

Data Annotation Tech Remote information

What is a data annotation tech remote?

Data Annotation Tech Remote jobs involve working from home or another remote location to label, tag, or classify data such as text, images, audio, or video. This work is essential for training and improving artificial intelligence and machine learning models. Data annotators use specialized software tools to accurately identify and categorize data according to specific guidelines provided by employers. These roles require attention to detail, consistency, and sometimes subject-matter expertise, depending on the project. Remote data annotation jobs are popular because they often offer flexible schedules and the ability to work from anywhere.

What are the key skills and qualifications needed to thrive as a data annotation tech remote?

To excel as a Data Annotation Tech (Remote), you need attention to detail, basic computer literacy, and familiarity with data labeling practices, often supported by a high school diploma or equivalent. Proficiency with annotation tools such as Labelbox, Supervisely, or proprietary platforms is typically required, and training in data privacy or quality assurance may be beneficial. Strong communication, time management, and the ability to focus independently are standout soft skills for this remote role. These competencies are crucial to ensure accurate, high-quality data labeling that directly impacts the effectiveness of AI and machine learning models.

What are some common challenges faced by remote data annotation techs, and how can they be addressed?

Remote Data Annotation Technicians often encounter challenges such as maintaining consistent annotation quality, managing repetitive tasks, and ensuring clear communication with team leads or project managers. To address these, it's helpful to establish a structured daily routine, use collaboration tools to stay connected with the team, and regularly review project guidelines to ensure accuracy. Many organizations also provide feedback loops and quality assurance checks, so being proactive in seeking feedback can help improve performance and job satisfaction.

What is the difference between Data Annotation Tech Remote vs Data Labeling Specialist?

AspectData Annotation Tech RemoteData Labeling Specialist
CredentialsBasic technical skills, sometimes certifications in data annotation toolsSimilar credentials, often with experience in labeling software
Work EnvironmentRemote, often freelance or contract-basedRemote or on-site, depending on employer
Industry UsageUsed across AI, machine learning, and data science companiesCommon in AI, autonomous vehicles, and tech firms

Both roles involve labeling data for machine learning models, with similar credentials and remote work options. The main difference lies in job titles used by employers, but their responsibilities and industry applications overlap significantly.

How much do remote data annotation tech jobs pay?

Remote data annotation technician jobs typically pay between $12 and $20 per hour, depending on experience, skill level, and the complexity of the annotation tasks. Some positions may offer additional benefits or flexible schedules, and pay rates can vary based on the employer and geographic location of the company or project. Entry-level roles often start at the lower end of the pay scale, while experienced annotators or those with specialized skills may earn higher wages.

What are the most commonly searched types of Data Annotation Tech jobs in Michigan?

The most popular types of Data Annotation Tech jobs in Michigan are:

What are popular job titles related to Data Annotation Tech Remote jobs in Michigan?

For Data Annotation Tech Remote jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Data Annotation Tech Remote jobs in Michigan look for?

The top searched job categories for Data Annotation Tech Remote jobs in Michigan are:

What cities in Michigan are hiring for Data Annotation Tech Remote jobs?

Cities in Michigan with the most Data Annotation Tech Remote job openings:

Infographic showing various Data Annotation Tech Remote job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 19% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

Senior Lead Data Engineer (Remote)

Three Rivers, MI • On-site, Remote

Stryker
Medical Equipment and Supplies Manufacturing • 10K+ employees

Full-time

Re-posted 11 days ago


Stryker rating

8.2

Company rating: 8.2 out of 10

Based on 113 frontline employees who took The Breakroom Quiz


Job description

Work Flexibility: Remote

As a Senior Lead, Data Engineering, you will serve as a technical leader who helps shape the future of enterprise data solutions. In this role, you will drive complex data initiatives, influence technical strategy, and partner with teams across the organization to build scalable, high-impact data products. This is an opportunity to solve challenging business problems while mentoring fellow engineers and elevating data engineering best practices.

What You Will Do

  • Lead the architecture, development, and modernization of scalable enterprise data platforms that support global procurement analytics and business transformation.
  • Define and help execute a multi-year data engineering strategy focused on platform scalability, reliability, automation, technical debt reduction, and long-term maintainability.
  • Design, build, and optimize Azure-based data solutions using technologies such as Databricks, Delta Lake, Azure Data Factory, Azure DevOps, CI/CD pipelines, and infrastructure automation.
  • Integrate and harmonize data across multiple ERP systems by standardizing supplier, purchasing, and master data into common enterprise data models.
  • Partner with procurement analysts, architects, engineers, and business stakeholders to translate complex business needs into reusable, scalable data products and engineering solutions.
  • Establish engineering standards, conduct architecture reviews, improve documentation, and mentor engineers to raise the overall technical capability of the team.
  • Identify and implement AI-enabled approaches that accelerate development, improve data quality, automate documentation, support testing, and enhance analyst productivity.
  • Evaluate and recommend tools, frameworks, patterns, and platform investments that improve performance, reliability, security, governance, and operational efficiency.
  • Support the development of internal data products, APIs, and user-facing applications that simplify access to procurement insights and improve analyst productivity.
  • Present technical recommendations, roadmap priorities, tradeoffs, and business impacts to technical and non-technical stakeholders, including leadership teams.

What you need

Required

  • Bachelor's degree in Computer Science, Data Engineering, Data Science, Information Systems, Mathematics, Statistics, or a related technical field.
  • 6+ years of experience in data engineering, analytics engineering, software engineering, or enterprise data platform development.
  • Proven experience architecting, building, and modernizing scalable cloud-based data platforms in an enterprise environment.
  • Hands-on experience with Azure-based data engineering technologies, including Databricks, Delta Lake, Azure Data Factory, Azure DevOps, CI/CD pipelines, and infrastructure automation.
  • Strong proficiency in SQL and Python, with experience using Spark or similar distributed data processing frameworks.
  • Experience designing reliable ETL/ELT pipelines, data warehouse or Lakehouse architectures, data models, and performance-optimized analytics solutions.
  • Experience integrating data from multiple ERP or enterprise source systems and harmonizing inconsistent master, supplier, purchasing, or transactional data into common data models.
  • Experience with API integrations, REST services, authentication, security, data governance, and production support practices.
  • Demonstrated ability to establish engineering standards, conduct architecture reviews, improve documentation, mentor engineers, and influence technical direction without direct authority.
  • Ability to partner with analysts, engineers, architects, and business stakeholders to translate complex business requirements into scalable, maintainable engineering solutions.

Preferred

  • Master's degree in Computer Science, Data Engineering, Data Science, Information Systems, Business Analytics, or a related technical field.
  • Experience supporting procurement, supply chain, manufacturing, finance, or ERP analytics in a global enterprise environment.
  • Experience building or modernizing enterprise data platforms, Lakehouse architectures, or cloud analytics ecosystems at scale.
  • Hands-on experience with multiple ERP platforms, such as SAP ECC, SAP S/4HANA, Oracle, JD Edwards, Infor, QAD, or Microsoft Dynamics.
  • Full-stack engineering experience, including React, Next.js, Vercel, API development, authentication, and internal web application development.
  • Experience using AI-assisted engineering tools and workflows, such as GitHub Copilot, ChatGPT, Claude, Cursor, AI coding agents, AI-assisted testing, or AI-assisted documentation.
  • Experience designing internal tools, data products, APIs, or analyst-facing applications that improve productivity and simplify access to insights.
  • Experience leading cloud modernization, platform migration, technical debt reduction, automation, or data quality improvement initiatives.
  • Experience with Power BI, Tableau, or similar business intelligence and visualization tools.
  • Demonstrated curiosity and continuous learning mindset, with a track record of experimenting with emerging technologies and applying them to practical engineering or analytics use cases.

United States of America Pay Ranges:

  • USN: $118,000 - $196,700 USD Annual
  • US5: $123,900 - $206,500 USD Annual
  • US10: $129,800 - $216,400 USD Annual
  • US15: $135,700 - $226,200 USD Annual
  • US20: $141,600 - $236,000 USD Annual
  • US30: $153,400 - $255,700 USD Annual
View the U.S. work location and transparency guide to find the pay range for your location.

Travel Percentage: 10%Stryker Corporation is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, gender identity, sexual orientation, national origin, disability, or protected veteran status. Stryker is an EO employer - M/F/Veteran/Disability.Stryker Corporation will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information.

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