Engineering Manager, Data Platform

We’re looking for an

Engineering Manager

to lead our data platform team and evolve our data ecosystem to reliably support personalization, experimentation, and analytics at scale. In this role, you will actively shape the mission and vision of the modern data platform and partner closely with Engineering, Product, Data Science, Machine Learning, Analytics, and Security to balance incoming business needs with long-term platform investments.

As a people manager, you’ll oversee the team of data and analytics engineers, ensuring their professional growth, providing technical guidance, and fostering a culture of ownership, accountability, and high engineering standards.

What you’ll do in particular:

• Own and define the long-term strategy and roadmap for our Data Platform, ensuring it supports personalization, experimentation, and analytics at scale.
• Establish and uphold standards for data quality, governance, reliability, and platform performance.
• Lead, mentor, and grow a high-performing team of data and analytics engineers.
• Drive clear prioritization across stakeholder requests while protecting foundational platform investments.
• Work closely with Product, ML, and Analytics teams to ensure the platform delivers trusted, well-defined, and accessible data that meets their evolving use cases.
• Continuously evaluate our data stack and infrastructure, recommending concrete improvements to architecture, tooling, and workflows to keep the platform scalable, reliable, and cost-efficient.

Requirements

About you:

• 2-5 years of people management experience leading data engineering or platform teams.
• 7\+ years of experience in data engineering, backend data systems, or platform engineering.
• Experience working on popular consumer applications with high-volume clickstream or event-driven data.
• Strong experience with modern data architectures (batch and streaming ingestion, warehouse/lakehouse modeling, orchestration, CI/CD for data).
• Deep experience with modern data warehousing or lakehouse architectures and leading migration or modernization efforts to improve scalability, performance, and cost efficiency.
• Experience with DBT and structured transformation practices.
• Experience supporting personalization, experimentation, or ML-driven products.
• Demonstrated ability to manage competing stakeholder demands while setting and protecting platform strategy.
• Expertise in DataOps and security.