Data Quality Engineer Job Description

Data Quality Engineer Job Description, Skills, and Salary

Get to know about the duties, responsibilities, qualifications, and skills requirements of a data quality engineer. You can use our job description template in this article to produce your own. We also provide you with information about the salary you can earn as a data quality engineer.


Who is a Data Quality Engineer?

A Data Quality Engineer ensures the accuracy and consistency of the data utilized by the company. They frequently deal with several other databases, apps, and other systems to ensure that all of this information is appropriately arranged and simple to find.

Data quality engineers handle the creation of novel techniques or procedures for enhancing the quality of data inside an organization. This might entail developing software tools or practices for confirming the correctness of incoming data or highlighting problems that require attention from human reviewers.

Data quality engineers will be in great demand as organizations rely increasingly on data-driven choices, and the necessity for high-quality data will only grow. This is due to their responsibility to ensure that the data organizations use them.

By acquiring a strong understanding of data quality assurance, data quality engineers may profit from this trend. They may then use their expertise to support businesses to ensure data accuracy and dependability. Data quality engineers might also concentrate on creating fresh approaches to enhancing data quality.

The importance of data in business is increasing, and data governance is receiving greater attention. Data management and organization in this context refer to the process of making data accessible and understandable. Data quality engineers can be crucial to this process by ensuring that data is reliable and consistent. They may also assist in developing data management rules and practices that will improve firms’ productivity and performance.


Data Quality Engineer Job Description

Below are the data quality engineer job description examples you can use to develop your resume or write a job description for your employee. The employer can use it to sieve out job seekers when choosing candidates for interviews.

The duties and responsibilities of the data quality engineer include the following:

  • Plan tasks, schedule, and perform quality testing tasks.
  • Develop and evaluate test tools to enhance the test process, test coverage, and test quality.
  • Give technical guidance and serve as a mentor to other engineers.
  • Use SQL queries and log file analysis to evaluate different ETL and data pipelines.
  • Validate the integrity, validity, and quality of the data across all data platform components;
  • Provide an automated test framework and tools;
  • Perform functional, integration, and load testing while automating regression testing to automate the data platform services and applications.
  • Express opinions to developers at meetings, especially the daily standups
  • Evaluate production logs to prioritize issues, then collaborate with the development team to find solutions;
  • Provide data on test case coverage and defects to support release choices.
  • Collaborate with the team leads and product managers to comprehend the requirements, develop the appropriate test cases, and run them.
  • Give technical and functional design comments and advocate vociferously for quality at each stage of the development process.
  • Work with the engineering and product management teams to identify, track down, and fix problems.



  • A high school certificate, GED, or its equivalent
  • A bachelor’s degree in computer science preferably, or any other discipline (optional)
  • Experience writing SQL queries and python
  • Knowledge of working with huge, diversified data sets in a big data setting


Essential Skills

Here are the skills you require to excel in your career as a data quality engineer:

  • Communication
  • Detail-orientation
  • ETL
  • Data Migration
  • Critical Analysis
  • Data Management
  • Independence and Team-playing
  • Data Modeling
  • Organizational
  • Database Management
  • Problem-solving
  • Data Analysis
  • SQL
  • Business Intelligence


Data quality engineers must be able to communicate effectively with other team members to share their thoughts and directives. When they speak with clients on the progress of projects or prospective problems that could develop, data quality engineers also employ communication skills. Data quality engineers might benefit from having excellent written and verbal communication skills by being able to convey complex ideas to non-technical audiences, which is frequently required in this industry.


Data quality engineers must be able to identify and repair mistakes in data sets. This takes attention to detail, since you may need to review enormous volumes of information to determine the source of a problem. Additionally, whether developing fresh data models or upgrading current ones, you pay close attention to detail.


Data quality experts employ the ETL, or extract-transform-load, the procedure to clean and organize massive volumes of data. To accomplish their jobs well, data quality engineers often need to be familiar with these procedures. They could also be in charge of instructing other workers on how to utilize ETL software correctly.

Data Migration

Data migration, or the transfer of data from one system to another, is a process that data quality engineers need to be familiar with. Data quality engineers apply this skill when working with software developers to construct new apps and programs that replace outdated ones. They also employ it while assisting businesses to move cloud storage providers or enhance their computer systems.

Critical Analysis

The ability to assess a problem and identify solutions is known as critical thinking. Data quality engineers apply critical thinking skills when examining data for correctness, consistency, and integrity. This skill is also used while developing new processes or procedures to guarantee the effectiveness and efficiency of their procedures.

Data Management

The capacity to oversee massive volumes of data and guarantee its accuracy is known as master data management. When building new databases or updating old ones, data quality engineers frequently employ master data management techniques. They could also require these abilities to ensure that their contact information, such as phone numbers and addresses, is accurate.

Independence and Team-playing

Data quality engineers frequently work alone, but they also cooperate with other organizational divisions. To comprehend how their function fits into the overall picture of the organization, they must be able to communicate clearly and listen to the viewpoints of others.

However, data quality engineers frequently work on tasks that need them to spend a lot of time alone, alone with data. They must understand when it is proper to ask for assistance from others or seek managerial direction.

Data Modeling

The process of developing a model for data storage is known as data modeling. Data modeling is used to make sure that data can be obtained and stored orderly. When building new systems or modifying current ones, data quality engineers employ data modeling. This ability enables them to design effective data storage systems that make it simple for others to locate information.


Being organized means being able to keep track of your tasks and supplies. Data quality engineers frequently work on many projects at once, so they must be able to efficiently manage their time and resources. Additionally, by keeping their workplace organized, individuals are better able to locate files when they are needed.

Data quality engineers may stay focused during meetings or presentations by being organized. Being organized might help them recall specifics later on if they need to take extensive notes or record information about new processes.

Database Management

Data quality engineers use data warehousing expertise to store and organize massive volumes of data. They could also develop a database that gives other workers easy access to the required data. To arrange all the data by subject, data warehousing requires building tables, columns, and rows in a relational database management system (RDBMS). This can ensure the information is correct and simple for others to discover when they need it.


Data quality engineers need problem-solving abilities to locate and fix problems in their job. These abilities may be used while assessing data, identifying the cause of mistakes or abnormalities, and coming up with fixes. When fixing the computers, software, and other technologies they employ on the job, data quality engineers also apply problem-solving techniques.

Data Analysis

Finding trends and patterns in data is the process of doing data analysis. When examining data to see if it is correct, comprehensive, or compatible with corporate requirements, data quality engineers use this expertise. To ensure that incoming data complies with these criteria, they also analyze data to find areas where their procedures may be improved.


Database management is done using the computer language SQL. When building data models, which are visual representations of how data should be kept in a database, data quality engineers utilize SQL. Additionally, they query the database using SQL, which means they inquire about its contents. They can use this to detect any problems with the content or structure of the database.

Business Intelligence

Analysis and interpretation of data are two skills that make up business intelligence. Data quality engineers use business intelligence to check incoming data for correctness, consistency, and completeness. They also employ it when they write reports that include conclusions or recommendations for improvement.


How to Become a Data Quality Engineer

Below are the steps to take to become a data quality engineer:

Step One: Obtain a Bachelor’s Degree

A bachelor’s degree in computer science, information technology, or a related discipline is required by some firms for a data quality engineer role. Database administration, software development and programming, artificial intelligence, machine learning, and data mining are some of the courses you may study as a part of your degree program that would be useful for this vocation.

To gain practical experience working with databases and other kinds of large-scale computer systems, it is crucial to select a school with a good engineering department. To aid in your understanding of how firms employ data analysis tools, you can take extra courses in business administration, statistics, and mathematics.

Note that prospective data quality engineers can still be hired by some organizations if they have the necessary skills for the role.

Step Two: Acquire Relevant Skills and Experience

Data mining, data warehousing, and business intelligence technologies are all skills that data quality engineers need to have. Companies utilize this kind of software to gather, store and analyze vast volumes of data. This kind of experience may be obtained through internships or entry-level IT jobs.

Additionally, you might be able to get work using these technologies at the computer science department of your college or a nearby startup business.

Step Three: Recognize Statistical Techniques for Data Analysis

Data quality engineers must be familiar with fundamental statistical concepts like probability theory and descriptive statistics. Understanding how to utilize these tools will enable you to see patterns in the data that could point to issues with accuracy or completeness. You might also need to conduct surveys or interviews to learn more about your client’s demands.

To assess whether there is a substantial difference between the two datasets, you should also be familiar with standardized tests. This information will assist you in determining when, in light of the findings of your analysis, action is required.

Step Four: Learn the Latest Technological Developments

Data quality engineers must be updated on new advances since technology is continuously evolving. You may need to attend conferences or read journals to learn about new technologies and best practices.

Step Five: Get an MBA

A master’s in business administration (MBA) with a focus on information systems is something you might want to think about pursuing.

Your career as a data quality engineer can improve with a master’s in business administration (MBA) with an emphasis on information systems. A crucial talent for data quality engineers who work with huge volumes of data is the ability to leverage technology and analytics to improve organizational performance, which you may learn in an MBA program.

Additionally, an MBA gives you leadership, project management, and communication skills, which are useful in various positions within a business.


Where to Work as a Data Quality Engineer

Data quality engineers operate in many different industries. Engineering, production, manufacturing, technology, finance, public service, transportation, food production, and petrochemicals all have a great demand for them. Data quality roles are necessary for every industry that relies on sales, and this position often reports to the IT division.


Data Quality Engineer Salary Scale

In the USA, the average income for a data quality engineer is $114,500 per year or $58.72 per hour. The starting salary for entry-level professions is $102,652 a year, while the average yearly salary for experienced workers is $155,250.

In the UK, the average income for a data quality engineer is £49,391 per year or £25.33 per hour. Most experienced professionals earn up to £65,000 yearly, while entry-level occupations start at £40,698.

A data quality engineer makes an average yearly pay of CA$68,231 in Canada.

In Germany, a data quality engineer has an average income of €48,817.

A data quality engineer makes an annual average pay of AU$75,008 in Australia.

In Ireland, a data quality engineer makes an average income of €41,994.

Data quality engineers’ salaries range from ₦143,000 to ₦469,000 monthly in Nigeria.

Science, Technology, and Engineering

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