YO IT Consulting
SQL QA Lead - Remote
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SQL QA Lead - Remote
SQL Quality Assurance Lead
Job Type: Contract Location: Remote
About the Role
In this hourly, remote contractor role, you will work as a SQL Quality Assurance Lead to oversee quality, consistency, and trainer performance across SQL and database AI training projects. You will:
- Review AI-generated SQL queries, database explanations, data-modeling content, and trainer/QA work
- Evaluate output quality against project guidelines
- Provide precise written feedback
- Ensure all contributors follow expected quality standards
Key responsibilities include:
- Assessing work for query correctness, database reasoning, schema understanding, join logic, aggregation accuracy, performance awareness, security awareness, readability, formatting, instruction-following, and adherence to project-specific rubrics
- Spotting recurring quality issues
- Communicating updates to trainers/QAs
- Supporting onboarding
- Maintaining documentation
- Activating contributors with inconsistent performance
This role is with a fast-growing AI Data Services company, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your SQL quality leadership will directly help improve the world’s premier AI models by ensuring that SQL training data is:
- Accurate
- Executable
- Logically sound
- Well-documented
- Aligned with client expectations
Selection process:
- AI interview
- Domain-specific task
- Interview with a recruiter
Important notes:
- No immediate project
- If qualified, you’ll be first expert reached out to when relevant opportunities arise
- Access to future projects through the company’s expert network
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Qualifications
-
Degree: Bachelor’s or Master’s degree in:
- Computer Science
- Data Science
- Information Systems
- Software Engineering
- Statistics
- Business Analytics
- Or equivalent professional experience
-
Language skills:
- Strong grasp of English to follow project guidelines
- Communicate with teams
- Provide clear technical feedback
-
Experience:
- 3+ years using SQL for:
- Analytics
- Backend development
- Database engineering
- BI
- Data warehousing
- Reporting
- QA
- Teaching
- Technical review
- 3+ years using SQL for:
-
Technical knowledge:
- Strong understanding of SQL fundamentals such as:
SELECTWHEREJOINsGROUP BYHAVINGORDER BY- Subqueries
- CTEs
- Window functions
- Indexes
- Constraints
- Transactions
- Normalization
- Strong understanding of SQL fundamentals such as:
-
Evaluation skills:
- Ability to judge SQL content against rubrics
- Identify issues such as:
- Incorrect joins
- Aggregation errors
- Duplicate counting
- Invalid syntax
- Inefficient queries
- SQL injection risks
- Dialect mismatches
- Incomplete explanations
-
Database and platform familiarity (preferred):
- PostgreSQL
- MySQL
- SQL Server
- SQLite
- BigQuery
- Snowflake
- Redshift
- Data warehouses
- Query plans
- ER modeling
- BI tools
-
Team leadership (strongly preferred):
- Experience leading or supporting remote teams of:
- Analysts
- Engineers
- Reviewers
- Annotators
- Educators
- QAs
- Experience leading or supporting remote teams of:
-
Tools and collaboration:
- Comfortable with:
- Discord
- Google Sheets
- Google Docs
- Trackers
- Dashboards
- GitHub
- Project management systems
- Comfortable with:
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-
Documentation and organization:
- Highly organized and able to maintain:
- Style guides
- FAQs
- Trackers
- Onboarding materials
- Honeypots
- Calibration tasks
- Documentation
- Highly organized and able to maintain:
-
Plus (strongly preferred):
- Experience with:
- AI training
- Data annotation
- LLM evaluation
- SQL QA
- Code review
- Rubric-based technical review
- Experience with:
Key Responsibilities
-
Spot-check SQL/database items to:
- Identify issues
- Provide feedback through direct messages (DMs)
- Escalate recurring or critical issues
-
Review AI-generated:
- SQL queries
- Database explanations
- Schema reasoning
- Analytics workflows
- Optimization recommendations
-
Update trainers/QAs via Discord with:
- Project guidelines
- Workflow updates
- SQL-specific quality expectations
-
Respond to inquiries including:
- Joins
- Aggregations
- Window functions
- Query dialects
- Schema design
- Performance
- Security concerns
- Rubric interpretation
-
Engage inactive contributors through:
- DMs
- Encouragement
- Track follow-ups
- Flagging availability issues
-
Create and maintain:
- SQL documentation
- Style guides
- Trackers
- FAQs
- Examples
- Honeypots
- Onboarding materials
-
Run onboarding/training calls for SQL contributors
-
**Flag unclear or problematic SQL:
- Misleading suggestions
- Non-executable queries
- Inefficient queries
- Insecure execution paths
- Dialect-incompatibilities
-
Identify and improve recurring quality gaps in SQL QA workflows
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