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How much do part time aws quicksight jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for part time aws quicksight in the United States is $70.06, according to ZipRecruiter salary data. Most workers in this role earn between $62.26 and $81.73 per hour, depending on experience, location, and employer.

What are part-time AWS QuickSight jobs?

Part-time AWS QuickSight jobs involve using Amazon QuickSight, a cloud-powered business intelligence service, on a flexible or reduced-hour schedule. These roles typically focus on creating data visualizations, dashboards, and reports to help organizations analyze their data. Part-time positions may include responsibilities such as connecting data sources, developing insightful analytics, and collaborating with teams to interpret results. This job is ideal for those who have experience with data analysis and want to work fewer hours while still contributing to data-driven decision making.

What are some common challenges faced by part-time AWS QuickSight professionals, and how can they be managed?

Part-time AWS QuickSight professionals often face the challenge of balancing multiple projects or clients while staying up-to-date with frequent updates to the QuickSight platform. Time management and clear communication with stakeholders are essential to ensure that project requirements and data visualization needs are met within limited working hours. Collaborating closely with data engineers and business analysts helps streamline workflows and clarify expectations, making it easier to deliver impactful dashboards and reports on schedule.

What are the key skills and qualifications needed to thrive as a Part Time AWS QuickSight professional, and why are they important?

To thrive as a Part Time AWS QuickSight professional, you need a solid background in data analysis, business intelligence, and experience with AWS QuickSight, often supported by a degree in computer science or a related field. Familiarity with cloud data sources, SQL, and AWS services like S3 and Redshift, along with QuickSight certification, is highly valuable. Strong problem-solving abilities, attention to detail, and effective communication skills help in translating business needs into actionable data visualizations. These skills ensure the delivery of insightful analytics and support informed decision-making for organizations leveraging cloud-based BI solutions.

What is the difference between Part Time Aws Quicksight vs Part Time Data Analyst?

AspectPart Time Aws QuicksightPart Time Data Analyst
Required SkillsData visualization, AWS Quicksight, basic SQLData analysis, SQL, Excel, visualization tools
CertificationsAWS certifications helpful but not mandatoryRelevant certifications like CAP, Microsoft Data Analyst beneficial
Work EnvironmentCloud-based, remote or on-site, tech-focusedOffice or remote, business or tech sectors
Industry UsagePrimarily in cloud and data visualization rolesAcross various industries including finance, marketing, healthcare

Part Time Aws Quicksight roles focus on creating data visualizations using AWS Quicksight, often requiring cloud and SQL skills. Part Time Data Analysts have broader data analysis responsibilities across industries, utilizing various tools. Both roles may be remote and part-time, but differ mainly in technical focus and tools used.

What are the most commonly searched types of Aws Quicksight jobs? The most popular types of Aws Quicksight jobs are:
Part Time Aws Quicksight jobs near you
Infographic showing various Part Time Aws Quicksight job openings in the United States as of June 2026, with employment types broken down into 99% Full Time, and 1% Nights. Highlights an 73% Physical, 7% Hybrid, and 20% Remote job distribution, with an average salary of $145,725 per year, or $70.1 per hour.
Adjunct Lecturer, Artificial Intelligence of Things (AIOT) (On-Campus, Fall '26)

Adjunct Lecturer, Artificial Intelligence of Things (AIOT) (On-Campus, Fall '26)

Columbia University

New York, NY • On-site

$11K - $13K/mo

Part-time

Posted 12 days ago

Job description

Company Description
Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds to pursue greater human understanding, pioneering discoveries, and service to society.
The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through twenty professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good.
Job Description
The School of Professional Studies seeks experienced industry professionals to serve as part-time Lecturer for a graduate-level course in Artificial Intelligence of Things (AIOT).
This course provides a comprehensive understanding of IoT technologies and their integration with AI and robotic systems. Students will explore IoT architecture, key components, and communication protocols, while gaining hands-on experience with IoT platforms, sensors, and data acquisition devices. The curriculum emphasizes practical AIoT applications for real-time decision-making in manufacturing, public safety, smart cities, healthcare, etc., and addresses the ethical considerations of these technologies.
Combining conceptual learning with practical assignments, the course features weekly lectures and readings on IoT fundamentals and applications, with biweekly quizzes to assess conceptual understanding. Students will further apply their learning through individual assignments and a group term project, ensuring a robust foundation in IoT analytics and AI-powered robotic automation.
Responsibilities
  • Lead class lectures, instructional activities, and classroom discussions. Attend all class sessions.
  • Monitor and address student concerns and inquiries.
  • Evaluate and grade student work and assessments.
  • Conduct office hours.

Qualifications
Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting.
Requirements
  • Doctoral degree in a relevant field (Computer Science, Electrical/Computer Engineering, Data Science, Applied Analytics, Information Systems, or closely related).
  • 7+ years of professional experience in one or more of: IoT/embedded systems, edge computing, cloud IoT platforms, data analytics/ML, or adjacent industries (e.g., smart devices, manufacturing/IIoT, healthcare IoT).
  • Proficiency with Python and Jupyter/pandas/matplotlib for analytics and data visualization
  • Strong knowledge of AI fundamentals and experience with machine learning applications
  • Strong communication and student mentorship skills; responsiveness on Slack/office hours; ability to manage hardware kits and demos.

Preferred Skills & Experience
  • University teaching experience, especially in graduate or professional programs.
  • University teaching experience in project-based or lab-based courses.
  • Proficiency with MicroPython for device programming
  • Practical skills with Thonny, UIFlow2, or VS Code, serial/USB drivers (e.g., CP210x), and a macOS/Windows terminal
  • Hands-on familiarity with ESP32-class microcontrollers (M5Stack Core2 preferred) and MQTT publish/subscribe patterns.
  • Working knowledge of AWS IoT Core (Things, certificates/policies, device shadows) and comfort with basic AWS services used for analytics (e.g., S3, QuickSight/Athena/Glue or equivalent).

Additional Information
Salary range: $11,000 - $13,000 per semester long course
Please submit a resume inclusive of university teaching experience.
All your information will be kept confidential according to EEO guidelines.
Columbia University is an Equal Opportunity Employer / Disability / Veteran

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