Data Quality Engineer Jobs
Data Quality Engineer jobs are open from entry-level to staff and principal across healthcare, fintech, e-commerce, and enterprise software, with specializations in data pipeline validation, metadata management, and observability. Scan the live roles below and apply to whichever ones fit.
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Quality Control (QC) Engineer – Data Center Construction
Location: Aurora, CO
Job Summary
We are seeking a Quality Control (QC) Engineer to execute technical inspections for a new Data Center construction project in Aurora, CO. This role focuses on Electrical QC, with supporting responsibilities in Mechanical and Plumbing (MPE) systems. You will be responsible for ensuring that all mission-critical infrastructure meets rigorous performance standards, local codes, and design specifications.
Key Responsibilities
- Field Inspections: Perform daily physical inspections of MPE installations. Verify that work-in-progress aligns with engineered drawings, project specifications, and National Electrical Code (NEC) standards.
- Testing Witness: Attend and document field testing of critical equipment (e.g., Switchgear, UPS, Generators, AHUs). Support Factory Acceptance Testing (FAT) and Site Acceptance Testing (SAT) protocols.
- Documentation & Reporting: Execute and maintain Inspection and Test Plans (ITPs). Generate daily QC reports, maintain punch lists, and verify that as-built documentation accurately reflects field conditions.
- Deficiency Management: Identify non-conforming work and issue Non-Conformance Reports (NCRs). Work directly with trade subcontractors to track repairs and verify successful re-inspections.
- Commissioning Support: Interface with commissioning agents to ensure the transition from installation to functional testing is seamless and documented.
- Compliance: Ensure all installations meet Uptime Institute Tier Standards and local Aurora, CO building department requirements.
Required Qualifications
- Experience: Minimum 1–2 years of field experience in Quality Control or Commissioning, specifically within mission-critical environments such as data centers, hospitals, or semiconductor plants. (Relevant Engineering degree may substitute for years of experience).
- Technical Proficiency: Strong mastery of Electrical systems (High/Medium/Low Voltage, UPS, STS, Generators) and a working knowledge of Mechanical/Plumbing systems (Chilled Water, CRAC units, EPO systems).
- Certifications: OSHA 30-hour Construction Safety certification is required. Valid Journeyman Electrician License or an Engineering degree is highly preferred. Familiarity with NETA (InterNational Electrical Testing Association) standards or ICC (International Code Council) Electrical Inspector certification is a plus.
- Software Skills: Proven experience using construction management and QC software such as Procore, BIM 360, or CxAlloy for documentation and punch-list management.
- Standards Knowledge: Deep understanding of the National Electrical Code (NEC) and Uptime Institute Tier Standards.
- Communication: Ability to clearly communicate technical deficiencies to subcontractors and collaborate with the City of Aurora Building Division for code compliance.
Job Type: Full-time
Pay: $75,000.00 - $100,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Paid time off
- Professional development assistance
- Relocation assistance
- Retirement plan
- Vision insurance
Application Question(s):
- Are you a US citizen?
Education:
- Bachelor's (Preferred)
Experience:
- electrical quality inspection: 2 years (Required)
Location:
- Aurora, CO 80019 (Preferred)
Ability to Commute:
- Aurora, CO 80019 (Required)
Work Location: In person
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Find Data Quality Engineer JobsData Quality Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software14
- Artificial Intelligence4
- Fintech2
- Manufacturing1
- Agriculture & Farming1
What Employers Look For
The qualifications that appear most often in data quality engineer jobs.
- Proficiency in SQL for writing data validation queries and profiling source datasets
- Experience with data quality or observability tools such as Great Expectations, dbt tests, or Monte Carlo
- Familiarity with data warehousing platforms such as Snowflake, BigQuery, or Redshift
- Ability to build and maintain data pipelines using Python or Spark
- Understanding of data governance concepts including lineage, cataloging, and metadata management
- Bachelor's degree in computer science, information systems, or a related quantitative field
Tips for Your Data Quality Engineer Job Search
Tailor your resume to data stack
List the specific tools you have used for data validation, profiling, and lineage tracking. Recruiters scanning data quality engineer resumes look for named tools over vague claims, so replace phrases like 'ensured data accuracy' with the actual frameworks and query languages you ran.
Highlight ownership of data contracts
Many job postings for this role ask for experience defining or enforcing data contracts between producers and consumers. If you have written SLAs, schema definitions, or quality thresholds that upstream teams were held to, call that out explicitly rather than burying it under general pipeline work.
Apply early to roles that fit
Migrate Mate lists data quality engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter by tech stack before you apply
Data quality tooling varies sharply by company. A role built on dbt and Great Expectations requires a different preparation than one centered on Informatica or Monte Carlo. Scan the job description for the monitoring and transformation layer before applying so your answers in screening calls land accurately.
Prepare for a SQL or pipeline take-home
Many data quality engineer interviews include a timed exercise where you identify anomalies in a sample dataset or write validation rules against a schema. Practice writing assertions, null checks, and referential integrity tests in SQL or Python so you are not working through the logic for the first time under pressure.
Negotiate scope alongside compensation
Data quality engineers are sometimes hired into reactive roles that only fix incidents rather than setting standards. Before accepting an offer, ask whether the role owns proactive monitoring design or only responds to downstream complaints. That distinction affects your growth and your ability to build a portfolio worth advancing from.
Data Quality Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most data quality engineers?
The most active employers for data quality engineers right now are Tesla, TikTok, and Reflection, and the most openings are in California, Texas, and New York, based on current listings on Migrate Mate as of November 2026. Healthcare systems, financial services firms, and large e-commerce platforms tend to post the highest volume of these roles year-round.
How many data quality engineer jobs are remote?
About 48% of data quality engineer openings are fully remote or hybrid as of November 2026, reflecting strong demand for distributed data teams. Roles focused on pipeline monitoring, dbt testing, and observability tooling are especially likely to be fully remote, while positions tied to on-premises data infrastructure or regulated healthcare environments more often require on-site presence.
How do you become a data quality engineer?
Start by building a strong foundation in SQL and understanding how data moves through pipelines from source to consumption layer. Learn at least one data quality framework such as Great Expectations or dbt tests and practice applying assertions to real datasets. Gaining experience with a cloud data warehouse and contributing to a data governance initiative, even in a junior analytics or data engineering role, positions you well to transition into this title directly.
Can you get a data quality engineer job with little experience?
Yes, you can break in by demonstrating hands-on work with validation logic even if it was not your official job title. Build a portfolio project where you profile a public dataset, identify anomalies, and document the rules you wrote to catch them. Many companies also hire junior data quality engineers from analyst or data engineering associate roles where you have already written tests or caught data issues in production.
What does the data quality engineer interview process look like?
Most processes start with a recruiter screen focused on your background and tool familiarity, followed by a technical phone interview where you walk through how you have approached a data quality problem in practice. A take-home exercise or live coding session testing your ability to write SQL validation logic or build assertions in a framework like Great Expectations is common at mid-stage. Final rounds typically include a system design conversation about how you would architect monitoring for a data pipeline and a cross-functional interview with stakeholders such as analytics engineers or data consumers.
Where can I find and apply to data quality engineer jobs?
You can find and apply to data quality engineer jobs on Migrate Mate, which lists current openings from across the United States. Search the roles available, find the ones that match your experience and preferred location or remote setup, and apply directly to each listing that fits.
See All 74 Data Quality Engineer Jobs
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