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# Data Engineer

- **Company:** Lightning AI
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $188,000.0 - $275,000.0
- **Contract:** Permanent contract
- **Skills:** Customer Data Management, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Warehousing, Python (Programming Language), Operational Data Store, SQL Databases, Data Lineage, Data Pipelines
- **Published:** July 17, 2026
- **Apply:** https://www.dice.com/job-detail/8e8f863c-da22-483c-94a1-34dabfcfeef0

## About the Role

* 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

## Description

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.
What You'll Do

 * 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


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