AI Data Platform Engineer

Introduction

We're looking for an AI Data Platform Engineer to join our purpose-driven, values-led team and take full ownership of the data platform behind Next Gen Learning (NGL). This is a hands-on technical role for an experienced engineer who thrives on ambiguity, owns systems end-to-end, and cares about what the numbers actually mean for the business.

Duties & Responsibilities

Data Model and Platform Ownership

• Own and evolve the core data model as the single source of truth for course, cohort, revenue and lead-generation analytics

• Shape that model into the Student 360 and Account 360 views the business is built around

• Generate ad-hoc data insights for stakeholders, and turn the ones that stick into sustainable, well-documented models the rest of the team can rely on

• Keep the platform trustworthy as the underlying product keeps changing, with automated testing, CI gates that block bad changes, drift alerts, and monitoring across data and pipelines

LLM Pipelines and AI Agents

• Build and maintain LLM-powered pipelines that run inside the data platform, such as company enrichment, sentiment scoring, and content classification

• Hold those pipelines to the same quality bar as any other: accuracy validation, CI gates, drift alerts, and monitoring in production

• Design the Student 360 and Account 360 records so internal agents can consume them safely, and build those agents, reading the same data under the same permissions and audit trail as any human user

Governance and Data Access

• Design and enforce data governance, including PII isolation, access control, and automated checks that catch documentation and schema drift before it ships

• Own how people and agents reach the data. Lightdash is today's self-service layer for non-technical teammates; you'll decide where that access layer goes next rather than treating any one tool as the destination

Collaboration and Stakeholder Partnership

• Partner directly with learning experience, marketing, finance and leadership to turn business questions into metrics, including the judgment calls a dashboard can't make on its own

• Serve a broad set of data consumers:

• Learning architecture and design: course quality, completion, NPS and live-session engagement

• Sales and marketing: lead-generation analytics, funnel and enrollment metrics, and audience enrichment

• Finance and leadership: revenue reporting and the trusted top-line numbers leadership plans against

• Faculty and course partners: clear, well-governed reporting on cohort performance and outcomes

• NGL's internal agents: first-class consumers of the data, not an afterthought

Desired Experience & Qualification

Who You Are

• Strong Software Engineer: You have solid engineering fundamentals: coding (e.g. Python), system design and architecture, code review, debugging, and testing

• Experienced Analytics / Data Engineer: You have several years of hands-on analytics or data engineering, with dbt in production, strong SQL, and production experience with BigQuery or a comparable cloud warehouse

• Comfortable with Messy, Live Systems: You can model a live, evolving OLTP schema, schema drift and undocumented tables included

• AI-Native Builder: You use AI-assisted engineering workflows (Claude Code, Cursor, Copilot) as your primary way of working. You're skilled in prompt engineering and context setting, and you've shipped LLM-based pipelines and agents with evaluation and production monitoring, not just a single API call

• Autonomous Owner: You have a track record of owning a data platform end-to-end with minimal oversight, and you're fluent in a git-based, CI/CD-driven workflow

• Creative Problem-Solver: You have the instinct to find a workable path through an ambiguous, fast-changing problem

• Clear Communicator: You turn data into something non-technical stakeholders can act on, and you communicate with clarity and confidence

• Start-up Ready: You're adaptable, hands-on, and motivated by building something meaningful from the ground up. You feel aligned to NGL company values

Bonus if you have

• Experience with a BI or semantic-layer tool, such as Lightdash, Looker, or the dbt Semantic Layer

• A background in ed-tech, marketplace, or subscription/cohort-based businesses

• Experience modeling Stripe or other billing/payments data

Interested?

Send your resume and a short note on a data platform you've owned (what it measured, who relied on it, and a decision it changed) to saara@nxgl.ai .

Should you not hear from us within 4 weeks, please consider your application unsuccessful.