SQL Team Lead

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.

You will 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. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, 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, and aligned with client expectations.

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. This will also provide you with access to future projects available through our expert network.

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.