Manager, Data Engineering
IruMiamiPosted 1 April 2026
Job Description
About Iru
Iru is the AI-powered security & IT platform used by the world’s fastest-growing companies to secure their users, apps, and devices. Built for the AI era, Iru unifies identity & access, endpoint security & management, and compliance automation—collapsing the stack and giving IT & security time and control back.
Iru is backed by some of the smartest investors in tech—General Catalyst, Tiger Global, Felicis, Greycroft, and First Round Capital. In July 2024, Iru raised $100 million from General Catalyst, valuing the company at $850 million. Customers include Notion, Cursor, Lovable, Replit, and Mercor, and Iru partners with industry leaders such as ServiceNow and AWS. Iru was named to Forbes’ America’s Best Startup Employers 2025 list for employee engagement and satisfaction.
The Opportunity:
We're looking for an Engineering Manager to lead this team. This is a player-coach role: you'll manage and grow the team while staying hands-on with technical work yourself. You should expect to spend ~40–60% of your time designing, building, and reviewing pipelines, models, and infrastructure alongside the team. You'll report to the Director of Data and partner directly with stakeholders across the business to ensure our data platform keeps pace with the company's growth.
This role is high-leverage. The systems you build and the decisions you make will shape how the entire company—and its AI capabilities—understands its products, customers, and performance.
About the Team:
The Data Engineering team owns the infrastructure and pipelines that power data-driven decisions across the company. We ingest, transform, and deliver centralized data from every product in our portfolio—making it possible for Data Science, Analytics, Finance, Revenue, and our product engineering teams to move fast and trust what they see.
Our work is foundational to the company's AI and machine learning initiatives. The quality, freshness, and structure of the data we deliver directly determines what's possible for the models and systems built on top of it. If you want to create leverage for AI at scale, this is where it starts.
Our stack includes Kafka, Airflow, Snowflake, dbt, Python, SQL, AWS. We're at an inflection point: the foundation has been built, and now we need leadership to drive the next chapter—modernizing our architecture, raising the bar on data quality, and scaling how we partner with the rest of the org.
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