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Data Cleaning Jobs in Michigan (NOW HIRING)

Webasto

Quality Internship

Plymouth, MI · On-site

$16 - $20.75/hr

Assisting with data cleaning, analysis, and structuring of reports. Audit Management: * Perform process audits against core documentation * Conduct process checks Qualifications * Currently pursuing ...

State of Michigan

Student Assistant

Lansing, MI · On-site

$17.10 - $29.57/hr

No 03 Do you have experience working with databases or digital information systems, including data entry, data cleaning, or standardizing datasets? * Yes * No 04 Have you completed coursework or ...

Corning

$104K - $125K/yr

You will collaborate with manufacturing teams, analysts, data scientists, and technology partners to deliver clean and reliable data for reporting, analytics, operational decision-making, and AI/ML ...

Ford Motor Company

Data Engineer

Dearborn, MI · Hybrid

$115K - $192K/yr

Proficiency in writing clean, maintainable code for data manipulation and automation. * GCP Expertise: Hands-on experience with BigQuery, dataflow, and Cloud Run . * Infrastructure Experience: Prior ...

Technomics

Data Analyst

Troy, MI · On-site

Analyze, clean, integrate, and validate data from multiple sources to support reporting, analysis, and operational workflows. * Develop queries, scripts, dashboards, applications, and other ...

Technomics, Inc.

Data Analyst

Troy, MI · On-site

Analyze, clean, integrate, and validate data from multiple sources to support reporting, analysis, and operational workflows. * Develop queries, scripts, dashboards, applications, and other ...

Domino's

Data Scientist

Ann Arbor, MI · On-site

Conducts data collection, cleaning, and preprocessing to prepare datasets for analysis. * Develops and implements basic statistical models and machine learning algorithms to address defined business ...

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All JobsData Cleaning JobsData Cleaning Jobs in Michigan

Data Cleaning information

What is a data cleaning?

A Data Cleaning job involves identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure high-quality data for analysis. This process includes removing duplicate records, filling in missing values, standardizing formats, and eliminating irrelevant or erroneous data. Data cleaning helps improve data accuracy, reliability, and usability for business intelligence, machine learning, and decision-making. Professionals in this role typically work with databases, spreadsheets, and data management tools to refine raw data into a structured and meaningful format.

What are the key skills and qualifications needed to thrive in data cleaning, and why are they important?

To thrive in Data Cleaning, you need a strong attention to detail, analytical skills, and a solid understanding of data management practices, often supported by training or coursework in data science, statistics, or information technology. Familiarity with tools like Microsoft Excel, SQL, Python (with libraries such as pandas), or specialized data cleaning software is highly valuable. Excellent problem-solving abilities, persistence, and effective communication are important soft skills for identifying and addressing data inconsistencies while collaborating with other team members. These skills are essential to ensure that datasets are accurate, reliable, and ready for analysis, leading to trustworthy business insights.

What are the most common challenges faced by professionals in data cleaning roles?

One of the biggest challenges in data cleaning is dealing with incomplete, inconsistent, or duplicate data from multiple sources, which often requires creative problem-solving and close attention to detail. Communicating with team members to clarify data definitions and intended use is also a frequent part of the job, as misinterpretations can lead to errors. Additionally, deadlines and large datasets can make the role fast-paced, so strong organizational skills and efficiency are important. However, overcoming these challenges offers valuable experience and plays a crucial role in ensuring the success of projects that depend on high-quality data.

What skills are needed for data cleaning?

Data cleaning requires skills in data analysis, attention to detail, and proficiency with tools like Excel, SQL, or data cleaning software. Knowledge of data formats, basic programming (e.g., Python or R), and understanding of data quality principles are also important for effective data cleaning tasks.

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

The most popular types of Data Cleaning jobs in Michigan are:

What are popular job titles related to Data Cleaning jobs in Michigan?

For Data Cleaning jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Data Cleaning jobs in Michigan look for?

The top searched job categories for Data Cleaning jobs in Michigan are:

What cities in Michigan are hiring for Data Cleaning jobs?

Cities in Michigan with the most Data Cleaning job openings:

Data Cleaning jobs near you
Infographic showing various Data Cleaning job openings in Michigan as of August 2026, with employment types broken down into 82% Full Time, 8% Part Time, and 10% Contract. Highlights an 79% In-person, 5% Hybrid, and 16% Remote job distribution.

12th Grade High School Math Teacher

Wayne County Schools Employment Network

Wayne, MI • On-site

Full-time

Posted 29 days ago

Job description

Full-Time 12th Grade High School Math Teacher

Personal Finance for Semester 1

Data Science & Graphic Literacy Second Semester

Personal Finance 

Prepare students for real-world financial success. This educator will implement our core personal financial literacy course, empowering students to make informed economic decisions. The course focuses on practical life skills, ensuring students master critical concepts like budgeting, debt management, and investing before graduation 

Key Responsibilities

  • Teach practical financial concepts including saving, banking, credit scores, debt, insurance, and taxes.
  • Integrate technology, financial simulation tools, and spreadsheet software into weekly lessons.
  • Translate complex economic theories into simple, age-appropriate, and actionable life skills.
  • Design project-based assessments, such as creating personal budgets or mock stock portfolios.
  • Align all instructional materials with state educational standards and graduation requirements.

Data science & Graphic Literacy

Introduces students to the critical cross-disciplinary skills of data fluency, statistical reasoning, and visual communication. The role bridges the gap between STEM and humanities, preparing students for college and 21st-century careers by teaching them how to consume, analyze, and create data-driven narratives.

Key Responsibilities

  • Curriculum Delivery: Teach students to read, analyze, and critique graphs, infographics, charts, and maps found in media and textbooks.
  • Hands-on Data Projects: Guide students through the full data lifecycle: collecting data, cleaning messy datasets, and identifying key trends.
  • Ethical Literacy: Train students to spot misleading data, biased axes, and manipulative visualizations in digital media.
  • Technical Training: Instruct students in beginner-friendly tools like Google Sheets, Canva, and introductory Python, R, or Tableau.
  • Classroom Management: Maintain an engaging, project-based high school classroom environment that supports diverse learning styles. 
  • College & Career Prep: Connect classroom concepts to growing career paths like data engineering, digital journalism, and graphic design.

Qualifications & Requirements

  • Education: Bachelor's degree in Mathematics, Data Science, Computer Science, Graphic Design, or a related field.
  • Certification: Valid State Teaching Certificate for Secondary Education (typically under Mathematics, Business and/or related area.).
  • High School Pedagogy: Proven ability to engage teenagers, manage classroom behavior, and break down complex concepts into bite-sized lessons.
  • Core Software Skills: Proficiency with spreadsheet software, basic data visualization platforms, and simple graphic design tools. [1]

Key Metrics for Success

  • Project Completion: High student success rates on final capstone projects, such as creating comprehensive, research-based infographics.
  • Critical Thinking Growth: Measurable improvement in student test scores regarding graph analysis and statistical bias detection.
  • Cross-Curricular Growth: Successful collaboration with history, science, or English departments on data-driven research papers.


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