AI Data Engineer

📋 Role Overview This role pairs two responsibilities that together determine the quality of our AI programming: building the data foundation the organization runs on, and sourcing the faculty who teach it. You will own the design and end-to-end implementation of Project Destined's Postgres database on PlanetScale — the system of record connecting participant, program, alumni, and employer data — while also identifying, recruiting, and onboarding the professors and technical instructors who lead our AI courses. You will collaborate across teams to ensure high-quality program delivery, strengthen our university partnerships, and make organizational data usable for AI applications and outcomes reporting. 🔑 Key Responsibilities ☑️ Database Architecture & End-to-End Build (PlanetScale Postgres) Own the design, implementation, and operation of Project Destined's core Postgres database on PlanetScale. •Design the schema covering participants, cohorts, programs, curriculum, attendance, assessments, alumni outcomes, and employer/partner records. • Stand up the full environment on PlanetScale Postgres: branching strategy, development and production branches, deploy requests, backups, and disaster recovery. Implement migration tooling and version control so schema changes are reviewable, reversible, and documented. • Establish indexing, query optimization, and performance monitoring practices; use PlanetScale insights to identify and resolve slow queries before they affect users. • Define access controls, roles, and data handling practices appropriate for student and alumni records. • Build and maintain the reporting layer and views the program, partnerships, and recruiting teams rely on. ☑️ Data Migration & Integration Consolidate fragmented organizational data into a single trusted source. • Inventory existing data across Airtable, Monday.com, Canvas, spreadsheets, CRM, and legacy systems, then plan and execute migration into Postgres. • Build and maintain ETL/ELT pipelines and API integrations that keep upstream and downstream systems in sync. • Implement validation, deduplication, and data quality monitoring with alerting on ingestion failures. • Maintain clear documentation and a data dictionary so the schema stays legible to non-technical teammates. ☑️ AI-Ready Data & Ingestion Pipelines Prepare organizational data for use in AI applications. • Structure and format datasets for AI use, including RAG pipelines, embeddings, and structured retrieval. • Build ingestion workflows for documents, CRM data, alumni data, and program content, with metadata tagging and access controls. • Support the alumni job recommendation engine by developing the underlying data foundations and matching signals from internal alumni and employer datasets. • Partner with the AI Engineer to ensure AI applications read from clean, current, well-governed data. ☑️ Faculty & Instructor Sourcing for AI Courses Build and manage the pipeline of professors and technical instructors who teach Project Destined's AI curriculum. •Identify and research prospective faculty across university computer science, data science, information systems, and business school programs, as well as industry practitioners with teaching experience. • Conduct outreach, screening conversations, and evaluation of subject matter expertise and teaching quality against course requirements. • Manage the end-to-end onboarding process: scoping, scheduling, agreements and honoraria coordination, platform access, and technical setup. • Maintain a structured faculty database — expertise areas, availability, courses taught, participant feedback — inside the systems you build. • Collect and analyze participant feedback on instruction, and use it to inform future faculty selection and course design. • Support course content validation and coordinate guest contributors and technical content reviewers as programming requires. ☑️ Cross-Functional Collaboration Work across teams to connect data infrastructure to program outcomes. • Translate operational needs into data models, queries, and reports that answer real questions. • Collaborate with recruiting and marketing to activate alumni visibility and partner engagement. • Coordinate with university partners on faculty relationships and academic alignment. 💡 Preferred Skill Set • Strong SQL and hands-on Postgres experience, including schema design, indexing, and query optimization • Experience operating a managed database platform (PlanetScale strongly preferred; Neon, Supabase, RDS, or similar considered) • Familiarity with modern full-stack and no-code/low-code tooling (Supabase, Lovable, Vercel, or equivalents) • Proficiency in Python for data pipelines, transformation, and scripting • Experience with ETL/ELT practices, API integrations, and migration from unstructured or semi-structured sources • Familiarity with AI ingestion workflows (RAG, embeddings, vector databases, structured knowledge systems) • Comfort with data governance: access controls, PII handling, auditability • Excellent written and verbal communication; the faculty sourcing component requires credible outreach to academic and industry professionals • Experience in academic program coordination, instructor recruiting, or university partnerships is a strong plus • Strong operational mindset: reliability, governance, scalability 💼 What We Offer • Opportunity to work at the forefront of the nation's largest and fastest growing real estate education and social impact platform. • A genuine greenfield build — you will design the data foundation the organization operates on for years, not maintain someone else's. • Hands-on exposure to program management, business development, and AI product innovation. • Direct collaboration with leading real estate firms, universities, and student leaders. • A dynamic, entrepreneurial team culture with room to grow and lead. ➡️ Next Steps Interested candidates should submit a resume along with a short note describing a database or data pipeline they designed and shipped end to end, including the schema decisions they would defend and any they would revisit.