SQL Team Lead

About this role

In this hourly, remote contractor role, you will work as a SQL Quality Assurance Lead (QAL) 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; and ensure that all contributors follow the expected quality standards.

SQL, Database QA, Query Optimization

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.

Qualifications

• 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.