Manager, Data Engineering

Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.

Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.

We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.

We're moving fast, and our data infrastructure needs to keep up. GPU telemetry, product usage, and operational signals are accumulating faster than our pipelines can surface them. We're at the stage where the right data foundation unlocks everything: better product decisions, faster model iteration, and cleaner ops. As our first Data Engineering Manager, you will help us get there.

This is an early, high-scope role reporting to our VP of Product. You'll own data reliability across the company: clean tables, documented lineage, and signal flowing to where it's needed. You drive projects from conception to production without waiting to be told what to do next.

• →Design and own ETL/ELT pipelines that move GPU telemetry, product usage, operational data, and customer data from where it lives to where it's useful
• →Build and maintain the clean, documented tables that analysts and data scientists actually work from — not raw dumps
• →Own data quality end-to-end: schema management, freshness monitoring, lineage tracking, and alerting when something breaks
• →Partner with engineering and GTM teams to instrument what isn't already tracked
• →Work closely with product, infra, and ML teams, primarily unblocking them

• 4+ years of experience managing analytics engineering or data engineering teams, preferably in a scaling startup environment
• 10+ years of total experience in analytics engineering, data engineering, or similar data-focused roles
• Deep expertise in data modeling, ETL pipelines, and data warehouse architecture
• Strong technical foundation with expertise in SQL, Python, and modern data stack tools (dbt, SQLMesh, etc)
• Proven track record of building and leading high-performing teams
• Experience partnering with Data Science, Product, and Engineering leaders to deliver key product metrics and user behavior insights
• Demonstrated ability to balance strategic thinking with hands-on technical leadership
• Strong communication skills with the ability to translate complex technical concepts for diverse audiences
• Experience scaling analytics functions from early stage to maturity in rapidly changing environments
• Track record of establishing data governance, quality standards, and best practices
• A bias for action and urgency, not letting perfect be the enemy of the effective
• A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end

We work in-person from our NYC office 2 days a week—we've found it accelerates how fast we ship.