AI Data Engineer – Build Postgres Platform & Recruit Faculty

About the role

Project Destined is looking for an AI Data Engineer who will both build the core data platform that powers its AI programs and lead the recruitment of professors and technical instructors for AI courses. You will own the end‑to‑end design, implementation, and operation of a PlanetScale Postgres database and ensure that organizational data is clean, integrated, and ready for AI applications.

Key responsibilities

• Design, implement, and operate the core Postgres database on PlanetScale, covering participants, cohorts, programs, curriculum, assessments, alumni outcomes, and partner records.
• Establish branching strategy, deployment workflow, backups, disaster recovery, and access controls for the database environment.
• Consolidate data from Airtable, Monday.com, Canvas, spreadsheets, CRM, and legacy systems into a single trusted source.
• Build and maintain ETL/ELT pipelines, API integrations, validation, deduplication, and data‑quality monitoring with alerting.
• Create indexing, query‑optimization, and performance‑monitoring practices using PlanetScale insights.
• Develop reporting layers, views, and a data dictionary for non‑technical stakeholders.
• Prepare datasets for AI use, including RAG pipelines, embeddings, and structured retrieval.
• Support the alumni job recommendation engine by providing clean, well‑governed data signals.
• Identify, recruit, and onboard professors and technical instructors for AI courses, collaborating with university partners.

Required profile

• Proven experience designing and operating Postgres databases in production.
• Strong background in data migration, integration, and ETL/ELT pipeline development.
• Ability to work cross‑functionally with product, partnership, and AI engineering teams.
• Experience sourcing and managing academic or technical talent is a plus.

Required skills

• PlanetScale Postgres
• SQL schema design and version control
• Database indexing, query optimization, and performance monitoring
• Backup, disaster recovery, and access‑control management
• ETL/ELT pipeline construction
• API integration
• Data validation, deduplication, and quality monitoring
• Data dictionary and documentation
• RAG pipelines, embeddings, and structured retrieval for AI