{"html":"<p>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.</p>\n<p>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.</p>\n<p>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.</p>\n<p>Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.</p>\n<p>Important:\nThere 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.</p>\n<h3>Responsibilities</h3><ul><li>Spot-check SQL/database items, identify issues, provide feedback through DMs, and escalate recurring or critical issues.</li><li>Review AI-generated SQL queries, database explanations, schema reasoning, analytics workflows, and optimization recommendations.</li><li>Update trainers/QAs on Discord about guidelines, workflow updates, and SQL-specific quality expectations.</li><li>Respond to questions around joins, aggregations, window functions, query dialects, schema design, performance, security, and rubric interpretation.</li><li>DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.</li><li>Create and maintain SQL documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials.</li><li>Run onboarding/training calls for SQL contributors.</li><li>Flag misleading, non-executable, inefficient, insecure, or dialect-incompatible SQL recommendations.</li><li>Identify recurring quality gaps and improve SQL QA workflows.</li></ul><h3>Requirements</h3><ul><li>Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, Software Engineering, Statistics, Business Analytics, or equivalent professional experience.</li><li>Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear technical feedback.</li><li>3+ years of experience using SQL for analytics, backend development, database engineering, BI, data warehousing, reporting, QA, teaching, or technical review.</li><li>Strong understanding of SQL fundamentals such as SELECT, WHERE, JOINs, GROUP BY, HAVING, ORDER BY, subqueries, CTEs, window functions, indexes, constraints, transactions, and normalization.</li><li>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.</li><li>Familiarity with PostgreSQL, MySQL, SQL Server, SQLite, BigQuery, Snowflake, Redshift, data warehouses, query plans, ER modeling, and BI tools is preferred.</li><li>Experience leading or supporting remote teams of analysts, engineers, reviewers, annotators, educators, or QAs is strongly preferred.</li><li>Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.</li><li>Highly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.</li><li>Experience with AI training, data annotation, LLM evaluation, SQL QA, code review, or rubric-based technical review is a strong plus.</li></ul>"}