SQL QA Lead - Remote
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Key skills for this role
About the Role
YO IT Consulting seeks a SQL Quality Assurance Lead for a remote contract role. You will oversee quality and trainer performance on SQL AI training projects, review AI-generated SQL queries, provide feedback, and maintain documentation.
Key Skills for This Role
Responsibilities
- Spot check SQL/database items, identify issues, provide feedback through DMs, and escalate recurring or critical issues
- Review AI generated SQL queries, database explanations, schema reasoning, analytics workflows, and optimization recommendations
- Update trainers/QAs on Discord about guidelines, workflow updates, and SQL specific quality expectations
- Respond to questions around joins, aggregations, window functions, query dialects, schema design, performance, security, and rubric interpretation
- DM inactive contributors, encourage activation, track follow ups, and flag availability issues
- Create and maintain SQL documentation, style guides, trackers, FAQs, examples, honeypots, and 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
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, Software Engineering, Statistics, Business Analytics, or equivalent experience
- Strong grasp of English language
- 3+ years of experience using SQL for analytics, backend development, database engineering, BI, data warehousing, reporting, QA, teaching, or technical review
- Strong understanding of SQL fundamentals (SELECT, WHERE, JOINs, GROUP BY, HAVING, ORDER BY, subqueries, CTEs, window functions, indexes, constraints, transactions, normalization)
- Ability to evaluate SQL content against rubrics and identify issues
- Familiarity with PostgreSQL, MySQL, SQL Server, SQLite, BigQuery, Snowflake, Redshift, data warehouses, query plans, ER modeling, and BI tools preferred
- Experience leading or supporting remote teams of analysts, engineers, reviewers, annotators, educators, or QAs strongly preferred
- Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems
- Highly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation
- Experience with AI training, data annotation, LLM evaluation, SQL QA, code review, or rubric based technical review is a strong plus
Full Job Posting
About This 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.
- Review AI generated SQL queries, database explanations, data modeling content, and trainer/QA work; evaluate output quality against project guidelines; provide precise written feedback.
- Assess 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.
- Spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors.
- Selection process involves an AI interview, a domain specific task, and an interview with a recruiter.
- Important: There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise.
Your Profile
- Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, Software Engineering, Statistics, Business Analytics, or equivalent professional experience.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear technical feedback.
- 3+ years of experience using SQL for analytics, backend development, database engineering, BI, data warehousing, reporting, QA, teaching, or technical review.
- Strong understanding of SQL fundamentals such as SELECT, WHERE, JOINs, GROUP BY, HAVING, ORDER BY, subqueries, CTEs, window functions, indexes, constraints, transactions, and normalization.
- 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, or incomplete explanations.
- Familiarity with PostgreSQL, MySQL, SQL Server, SQLite, BigQuery, Snowflake, Redshift, data warehouses, query plans, ER modeling, and BI tools is preferred.
- Experience leading or supporting remote teams of analysts, engineers, reviewers, annotators, educators, or QAs is strongly preferred.
- Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
- Highly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.
- Experience with AI training, data annotation, LLM evaluation, SQL QA, code review, or rubric based technical review is a strong plus.
Key Responsibilities
- Spot check SQL/database items, identify issues, provide feedback through DMs, and escalate recurring or critical issues.
- Review AI generated SQL queries, database explanations, schema reasoning, analytics workflows, and optimization recommendations.
- Update trainers/QAs on Discord about guidelines, workflow updates, and SQL specific quality expectations.
- Respond to questions around joins, aggregations, window functions, query dialects, schema design, performance, security, and rubric interpretation.
- DM inactive contributors, encourage activation, track follow ups, and flag availability issues.
- Create and maintain SQL documentation, style guides, trackers, FAQs, examples, honeypots, and 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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