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SQL QA Lead - Remote
SQL Quality Assurance Lead
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.
You will assess work on:
- Query correctness
- Database reasoning
- Schema understanding
- Join logic
- Aggregation accuracy
- Performance awareness
- Security awareness
- Readability
- Formatting
- Instruction-following
- Adherence to project-specific rubrics
Key responsibilities include:
- Spotting recurring quality issues
- Communicating updates to trainers and QAs
- Supporting onboarding
- Maintaining documentation
- Activating contributors who are not working consistently
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 training data is accurate, executable, logically sound, well-documented, and aligned with client expectations.
Selection Process
Selection involves:
- An AI interview
- A domain-specific task
- An interview with a recruiter
Important: There is no immediate project for this role; however, if qualified, you will be among the first experts reached for future opportunities. This role provides access to future projects available through our expert network.
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Your Profile
Education
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
Technical Experience
- 3+ years of experience using SQL for:
- Analytics
- Backend development
- Database engineering
- BI
- Data warehousing
- Reporting
- QA
- Teaching
- Technical review
Knowledge of SQL Fundamentals
Strong understanding of:
SELECT,WHERE,JOINsGROUP BY,HAVING,ORDER BY- Subqueries
- CTEs (Common Table Expressions)
- Window functions
- Indexes
- Constraints
- Transactions
- Normalization
Experience Reviewing SQL/content
Ability to evaluate SQL content against rubrics and identify issues such as:
- Incorrect joins
- Aggregation errors
- Duplicate counting
- Invalid syntax
- Inefficient queries
- SQL injection risks
- Dialect mismatches
- Incomplete explanations
Preferred Experience
Familiarity with:
- PostgreSQL, MySQL, SQL Server, SQLite, BigQuery, Snowflake, Redshift
- Data warehouses
- Query plans
- ER (Entity-Relationship) modeling
- BI tools
Leadership and Collaboration
Experience leading or supporting remote teams of:
- Analysts
- Engineers
- Reviewers
- Annotators
- Educators
- QAs
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Tools Proficiency
Comfortable with:
- Discord
- Google Sheets
- Google Docs
- Trackers/dashboards
- GitHub
- Project management systems
Organizational Skills
- Highly organized
- Maintain:
- Style guides
- FAQs
- Trackers
- Onboarding materials
- Honeypots
- Calibration tasks
- Documentation
Bonus Considerations
Experience with:
- AI training
- Data annotation
- LLM evaluation
- SQL QA
- Code review
- Rubric-based technical review
Key Responsibilities
-
Spot-check SQL/database items, identify issues, and escalate recurring or critical issues via direct messages (DMs).
-
Review AI-generated SQL queries, database explanations, schema reasoning, analytics workflows, and optimization recommendations.
-
Update trainers/QAs on Discord about:
- Guidelines
- Workflow updates
- SQL-specific quality expectations
-
Respond to questions on:
- Joins
- Aggregations
- Window functions
- Query dialects
- Schema design
- Performance
- Security
- Rubric interpretation
-
Engage with inactive contributors:
- DM encouragement to activate
- Track follow-ups
- Flag availability issues
-
Create and maintain documentation and resources:
- SQL documentation
- Style guides
- Trackers
- FAQs
- Examples
- Honeypots
- Onboarding materials
-
Run onboarding/training calls for SQL contributors.
-
Flag misleading, non-executable, inefficient, insecure, or dialect-incompatible SQL recommendations.
-
Identify recurring quality gaps and improve SQL QA workflows.
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