DATA QUALITY ANALYST JOB DESCRIPTION

In general, you are responsible for ensuring that the data the organisation uses is accurate, complete and consistent.

Below is the typical workload data quality analysts undertake on a daily basis:

Identifying data quality issues: This entails using a variety of tools and techniques such as data profiling and cleansing. It is likely that you will work with other professionals to develop metrics that can be used to measure the accuracy, completeness and consistency of the data.

Analyse data quality issues: When problems with data integrity are discovered, you will analyse them to determine what caused them. This may involve working with other members of the organisation such as data stewards to understand how the data is collected, processed and stored.

Develop data quality rules and controls: You will create these based on your research to help avoid similar problems in the future. These guidelines might entail building data validation procedures, quality checks or profiling reports. Once created, it is your job to ensure adherence to regulatory requirements, data governance and documentation workflows.

Monitor data quality: This amounts to assessing and reviewing the data to ensure that it meets the organisation's standards. This may involve developing and running tests or using dashboards to track the health of the data.

Work with stakeholders and Customers: This could entail informing decision-makers about the implications of issues, working with data stewards to resolve data quality problems or creating educational resources to help other organisation members and even clients.

Collaborate with other data professionals: To ensure that data quality is integrated across the entire data lifecycle, it is necessary for you to work closely with other professionals including data analysts and engineers. This may involve collaborating on data modelling, Agile or working with data architects to ensure that quality is considered during the design of new systems.

Becoming a Data Quality Analyst (DQA) can be an interesting journey, with numerous forks in the road and branching paths. The first step in embarking on this career is to obtain a relevant degree in a field such as computer science, data science, statistics, or mathematics. However, this is only one of many potential paths to pursue, as you may also be able to get started with a degree in a related field, such as business or economics and then gain technical skills through on-the-job training or additional coursework. 

Once you've obtained your degree, you may start your journey as a junior data analyst, a position that serves as a foundational stone upon which to build your future. In this role, you'll be tasked with handling data and verifying its quality and completeness. As you navigate this position, you'll begin to learn about the tools and techniques used to maintain data quality, gradually becoming more proficient in their use.

After several years of working as a junior data analyst, you may be ready to ascend to the next level of the DQA career path. As a Data Quality Analyst, you'll be tasked with the challenge of monitoring data quality, identifying issues and implementing solutions to improve data accuracy, completeness and consistency. This position requires a deep understanding of the intricacies of data management and you'll need to be able to navigate data analysis with precision and skill.

If you've successfully navigated the challenges of the Data Quality Analyst role, you may be ready to ascend to the next level of the data quality hierarchy: the Data Quality Team Lead. In this position you'll be responsible for managing a team of Data Quality Analysts and overseeing their work to ensure that data quality goals are met. In this role, you'll need to have a firm grasp of the complexities of data analysis and be able to lead your team with confidence and authority through the twisting paths of data management.

The next level of the DQA career path is the Data Quality Manager. This role requires a high degree of strategic thinking, as you'll be responsible for developing and implementing data quality strategies, managing data quality projects and ensuring that data quality standards are being met across the organisation. This position requires a deep understanding of the business context in which data quality operates and you'll need to be able to navigate corporate politics with ease.

Finally, the apex of the data quality hierarchy is the Director of Data Quality. In this role, you'll be responsible for leading the entire data quality function within an organisation. You'll oversee the work of multiple teams of Data Quality Analysts and Managers and you'll be responsible for ensuring that data quality is a top priority across the organisation. This position requires a rare combination of technical skill, strategic thinking and leadership ability. 

Overall, a career as a Data Quality Analyst is certainly challenging, but for those with an insatiable thirst for knowledge and a passion for ensuring data accuracy and completeness, it can be a rewarding and exhilarating journey. With experience and education, you can progress from entry-level positions to the upper echelons of leadership, guiding organisations through the labyrinth of data quality and helping them make better business decisions.

Here are some pointers on how to begin on your career path:

Build your foundational skills: A degree in computer science, data analytics or a related field can provide you with a strong foundation in data management and analysis. In addition, experience working with databases, data analysis tools and programming languages like SQL and Python can help you gain a better understanding of data quality and management.

Job Search: Numerous businesses provide entry-level jobs or internships for those beginning their careers in this field. These positions can provide you with hands-on experience and help you build your skills in a real-world setting.

Network/Events: Research and attend industry events and conferences to network with professionals in the data field. This can help you learn about the latest trends and technologies and make valuable connections that can lead to job opportunities.

Portfolio: This coincides with accumulating several years of experience. Creating a portfolio of your work can help you stand out to potential employers. This can include examples of data quality assessments, reports and other projects that demonstrate your skills in data analytics and analysis.

Soft skills: In addition to technical ability, employers also look for candidates who possess strong communication, problem-solving and collaboration skills.

Industry trends: Data management is an ever-evolving field, so it's crucial to be proactively looking ahead and staying relevant with the latest trends, insights and technologies by considering online courses or attending webinars.

Be persistent and patient: Landing your first job as a Data Quality Analyst can take time and effort. Don't get disheartened if you do not receive an immediate response after applying for your desired job. Keep honing your skills, building your network and staying up to date with the latest trends. Eventually, the right opportunity will come along.

IIn this role there is a diverse set of technical, analytical and interpersonal skills required in order to succeed.

Here are some of the most important ones:

Attention to detail: Your job as a Data Quality Analyst is all about ensuring the accuracy and completeness of data. This requires a keen eye for detail, as even the smallest errors can have significant consequences.

Statistical and Analytical skills: You need to be able to identify data quality issues and determine the root cause of those issues. In addition, you should be comfortable with analysis tools and have a good understanding of data concepts.

Communication skills: Working closely with other members of your team, plus stakeholders from other departments is a key requirement. Excellent communication skills will be required in order to explain complex technical issues in a clear and concise manner.

Problem-solving skills: Being able to think creatively and come up with innovative and timely solutions to these problems is desirable.

Extensive Technical skills: The ability to gain a good understanding of database systems, SQL and data tools is essential. Utilising data profiling and quality assessment tools, plus data cleansing and standardisation software is normally part and parcel of such roles.

Business acumen: It's important to have an understanding of the business context in which you are working, so that you can prioritise data quality issues and understand the impact that data quality has on business outcomes.

Latest Trends: The industry is constantly developing. Therefore, it's important to stay relevant with the latest technologies and methodologies. Commit to continuous learning in order to progress your professional development.