In This Article:
What a data quality engineer does
A data quality engineer ensures reliable, high quality data is delivered to internal and external stakeholders and applications. These specialists are also commonly referred to as data reliability engineers.
To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders. These data consumers could include business executives, developers, or even other data teams.
This is critical because data curation is costly and data quality is subjective. A financial dashboard may need to be accurate within a penny while a machine learning model may only need directionally accurate (but constantly refreshed) data. The most common use case data quality engineers support are:
- Analytical dashboards: Mentioned in 56% of job postings
- Machine learning or data science teams: Mentioned in 34% of postings
- Gen AI: Mentioned in one job posting (but really emphatically).
The data quality engineer will then need to design or optimize data architectures and pipelines to ensure these requirements are met (mentioned in 70% of job postings).
This is a continuous process as data environments are complex, interdependent, and constantly changing. To account for this, data quality engineers will monitor data quality by designing and deploying data testing at scale (mentioned in 83% of job postings).
Organizations are placing an emphasis on the communication skills of data quality engineers with 74%% specifying the need to advocate for data quality across teams and 43% specifically mentioning collaborating with developer teams. Interestingly, only 43% specifically mention resolving the data quality issues that do arise.
The skills, languages and tools of a data quality engineer
Data quality engineers need to be highly skilled in multiple programming languages such as SQL (mentioned in 61% of postings), Python (56%), and Scala (13%). About 61% request you also have a formal computer science degree.
Data quality engineers also need to have experience operating in cloud environments and using many of the modern data stack tools that are utilized in building and maintaining data pipelines. 78% of job postings referenced at least part of their environment was in a modern data warehouse, lake, or lakehouse.
In terms of specific processing techniques for large scale data, 13% of job postings mentioned Spark, 17% mentioned Kafka/Kinesis, 17% mentioned Hadoop, and one posting referenced S3.
As if that isn’t enough, slightly less than half of the job postings requested strong knowledge of agile development or DevOps project management techniques as well as knowledge of a specific domain (like healthcare data).
Example data quality engineer job description
One of the most representative and well rounded data quality engineering job postings I reviewed came from Fidelity Talent Source which is truncated a bit below:
You will contribute as a key member of our fast-paced team in the testing of data capabilities that support applications across the enterprise. These applications & technologies may include but are not limited to: PowerBI, Tableau, and other BI tools, our external corporate website, intranet site, proprietary employee management and other HR systems, and data feeds that may flow between these and other systems.
Expect to use your knowledge of development and testability to effect better design, promote accurate engineering decisions, and implement bug prevention strategies which are both scalable and maintainable across this set of key responsibilities:
- Collaborate with cross-functional teams to ensure quality and timely delivery of products.
- Develop and execute manual and automated test cases for software applications to ensure the reliability of data pipelines, ETL processes, and data transformations.
- End to end integration testing between multiple independent systems and interfaces (flat files, APIs (ETL), etc.
- Utilize AWS services, Snowflake data warehouse, and ODS to test data integration and migration processes
- Conduct functional, integration, regression, and performance testing of database systems using industry standard technologies (SQL, Python, iCEDQ, etc.)
- Create and maintain documentation of test plans, test cases and testing results.
The Expertise and Skills You Bring
- Engage with product owners and development leads to create testing strategies
- Identify areas of improvement in data quality processes and propose solutions to enhance data accuracy and reliability.
- Collaborate with data engineering and development teams to implement data quality best practices and optimize data workflows.
- Document data quality issues, testing procedures, and resolutions for future reference and knowledge sharing.
- Experience with data processing concepts like mapping documents and complex data relationships.
- Experience with requirement analysis, defect tracking, coordinating with team members in different locations, and test reporting and signoff.
- Strong analytical and technical skills to address sophisticated issues.
- Ability to perform root cause analysis on defects.
- Assist in developing and maintaining data governance policies and standards.
- Design, monitor and maintain QA reports, KPIs & quality trends for the internal data systems
- Detailed and organized approach to solving problems.
- Communication skills that prioritize collaboration over conflict.
- Previous experience with Agile methodology
Data quality engineer salary
Glassdoor reports the average annual salary for a data quality engineer is about $113,556. That aligns with my analysis of job postings which found an average annual salary of about $107,941.
However, you will likely get paid more if you show up in the office. In-person job postings were listed for an average of around $125,000 while remote positions were listed at an average annual salary of $92,000.
Data quality engineer career path and future demand
While data quality is frequently the responsibility of everyone across the data team, typically a specific role will have accountability. Based on our recent poll of 200 data professionals the most common owners of data quality within an organization are:
- Data engineers: 50%
- Data analysts: 22%
- Software engineers: 9%
- Data reliability/quality engineers: 7%
- Analytics engineers: 6%
- Data governance professionals: 5%
- Business stakeholder: 3%
Organizations often choose to specifically define and fill a role for a data quality engineer when they have larger data teams that can benefit from specialization or data quality has a particularly large or direct impact on business value.
The latter case tends to be when data is customer facing; supporting critical operations;
powering machine learning applications; guiding AI models; or data IS the product. In the job postings we reviewed, these organizations tended to be particularly clustered within the healthcare, government contracting, finance, and IT industries.
Data quality engineers will have a stronger focus and specialization on data quality than their data engineering colleagues and be more systems focused than their business minded data quality analyst peers.
This combination can make them particularly effective at improving data quality processes and resolving data quality incidents at scale. For example, after Mercari deployed a data reliability engineering team, they saw their data incident status update rate jump from about 10% to close to 100%.
Whether the role is called a data quality engineer, data reliability engineer, or just a plain data engineer, it’s clear the demand for improving data quality is only going to increase. The data professionals that can master these skills, tools, and techniques will likely have a bright and fulfilling career.
Are you a data quality engineer or data professional looking for something better than data testing to ensure data quality? Talk to us!
Our promise: we will show you the product.