Engineering Manager, Data & Analytics

About the Role

We are looking for an experienced Engineering Manager, Data & Analytics to lead a team responsible for operating, scaling, and evolving our large-scale advertising data and analytics ecosystem. 
The team owns business-critical real-time and batch data systems supporting real-time reporting, scheduled reporting, ad-hoc analytics, log-level data, audiences, ad-serving use cases, observability, and machine-learning feedback loops.
This role requires a strong combination of technical leadership, people management, execution, and stakeholder management. The Engineering Manager will work closely with Engineering, Product, Business, Analytics, and ML teams to evolve the existing ecosystem while maintaining reliability, scalability, performance, and cost efficiency.

What You'll Do

Engineering & Technical Leadership

• Own the technical direction and evolution of large-scale real-time and batch analytics systems.
• Scale streaming workloads built on technologies such as Kafka and Spark Streaming.
• Evolve analytical and data-processing workloads across Snowflake, HDFS, and object storage.
• Support real-time, scheduled, ad-hoc, and log-level reporting at advertising scale.
• Drive improvements in scalability, reliability, performance, data quality, and infrastructure efficiency.
• Modernize components of the existing ecosystem while protecting business-critical workloads.
• Strengthen observability, monitoring, data lineage, SLAs/SLOs, and operational excellence.
• Enable data capabilities supporting audiences, ad serving, experimentation, ML training and feedback loops, and AI-driven use cases.
• Evaluate appropriate adoption of CPU/GPU accelerated computing and emerging data and AI technologies.Drive architecture reviews and establish strong engineering standards across the team.

People & Execution Leadership

• Lead, mentor, and grow a high-performing engineering team.
• Hire and develop engineers and create strong technical leaders within the organization.
• Establish clear ownership and accountability across critical systems.
• Own roadmap planning, prioritization, execution, and delivery.
• Balance new capabilities with technical debt, platform modernization, reliability, and operational commitments.
• Lead incident reviews and drive systemic fixes to prevent recurring production problems.
• Foster an engineering culture focused on ownership, technical excellence, simplicity, and measurable outcomes.