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SQL Quality Assurance Lead (QAL)
United States
Up to $70/hr
June 20, 2026
Job description
Role Overview
As a SQL Quality Assurance Lead (QAL), you will oversee quality, consistency, and contributor performance across SQL and database AI training projects.
You will review AI-generated SQL queries, database explanations, data-modeling content, as well as trainer and QA work, evaluate outputs against project guidelines, provide detailed written feedback, and ensure adherence to quality standards.
This role combines database expertise, quality management, contributor support, documentation, and process improvement responsibilities within large-scale AI training programs.
Your work will directly contribute to improving some of the world's leading AI models by ensuring SQL training data is accurate, executable, logically sound, well-documented, and aligned with client expectations.
Key Responsibilities
Quality Monitoring
- Review and spot-check SQL and database training items
- Identify quality issues and recurring patterns
- Provide feedback to contributors
- Escalate critical quality concerns when necessary
SQL & Database Review
Review AI-generated:
- SQL queries
- Database explanations
- Schema design content
- Analytics workflows
- Query optimization recommendations
- Data-modeling solutions
Verify:
- Query correctness
- Join logic
- Aggregation accuracy
- Schema understanding
- Performance considerations
- Security awareness
- Instruction compliance
Trainer & QA Communication
Communicate updates regarding:
- Guidelines
- Workflow changes
- Project requirements
- SQL quality expectations
Support contributors through Discord and other communication channels
Question Handling
- Answer questions related to:
- JOINs
- Aggregations
- Window functions
- Query dialects
- Schema design
- Query optimization
- Database security
- Rubric interpretation
Contributor Activation
- Contact inactive contributors
- Encourage project participation
- Track follow-ups and contributor availability
Documentation
- Create and maintain:
- Style guides
- FAQs
- Quality trackers
- Onboarding materials
- Calibration tasks
- Quality notes
- Example libraries
- Honeypots
Onboarding & Training
- Conduct onboarding sessions
- Explain:
- Project workflows
- Rubrics
- Quality expectations
- SQL and database standards
Quality Alignment
- Ensure consistent application of SQL quality standards
- Communicate project updates and evolving requirements
Process Improvement
- Identify recurring quality issues
- Recommend workflow improvements
- Help build scalable SQL QA processes
Risk & Query Quality Review
- Flag:
- Invalid SQL syntax
- Incorrect joins
- Duplicate counting issues
- Aggregation errors
- SQL injection risks
- Inefficient queries
- Dialect incompatibilities
- Incomplete explanations
Required Qualifications
- Bachelor's or Master's degree in:
- Computer Science
- Data Science
- Information Systems
- Software Engineering
- Statistics
- Business Analytics
OR equivalent professional experience.
- Strong English communication skills
- Minimum 3 years of experience in:
- SQL development
- Analytics
- Database engineering
- Data warehousing
- Reporting
- BI
- QA
- Teaching
- Technical review
SQL & Database Expertise
Strong understanding of:
- SELECT
- WHERE
- JOINs
- GROUP BY
- HAVING
- ORDER BY
- Subqueries
- CTEs
- Window Functions
- Indexes
- Constraints
- Transactions
- Normalization
Evaluation Skills
Ability to identify:
- Incorrect joins
- Aggregation mistakes
- Duplicate counting
- Invalid syntax
- Inefficient queries
- SQL injection vulnerabilities
- Dialect mismatches
- Incomplete explanations
Preferred Experience
Experience with:
- PostgreSQL
- MySQL
- SQL Server
- SQLite
- BigQuery
- Snowflake
- Amazon Redshift
- Data Warehouses
- Query Plans
- ER Modeling
- BI Tools
Additional advantages include:
- AI training
- Data annotation
- LLM evaluation
- SQL QA
- Code review
- Rubric-based technical review
Skills & Technologies
- SQL
- Database QA
- PostgreSQL
- MySQL
- Query Optimization
- Data Analysis
- Code Review
- LLM Evaluation
- AI Training
- Trainer Feedback
Selection Process
- AI Interview
- Domain-specific task
- Recruiter interview
Important Note
There may not be an immediate project assignment. Qualified candidates will join the expert network and may be contacted for future opportunities.
Why Work with SME Careers?
Shape the Future of AI
Your work directly impacts how AI systems learn and communicate.
Flexible Work
Choose your own schedule and work remotely.
Weekly Payments
Receive payments once approved work is completed.
Community Rewards
Earn referral bonuses through the SME Careers referral program.
About SME Careers
SME Careers is an AI Data Services company and subsidiary of SuperAnnotate.
The company supports many of the world's largest AI organizations by providing high-quality training data and expert evaluation services for frontier AI systems.
Apply now
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