Engineering Manager, Data & Analytics

Job description

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. Stakeholder Leadership • Partner closely with senior stakeholders across Engineering, Product, Business, Analytics, Trading, and Machine Learning. • Translate complex business requirements into scalable technical strategies. • Lead challenging discussions involving priorities, timelines, technical limitations, architecture, cost, and competing business requirements. • Challenge assumptions and requirements when necessary and provide data-driven alternatives. • Clearly communicate technical trade-offs, risks, and investment requirements to technical and non-technical leadership. • Build alignment across teams where ownership and priorities may not naturally align. We'd Love for You to Have • 10+ years of engineering experience, with significant experience leading and managing engineering teams. • Proven experience hiring, mentoring, developing, and growing engineers and technical leaders. • Strong technical background in large-scale distributed data and analytics systems. • Hands-on architectural understanding of technologies such as Kafka, Spark/Spark Streaming, Snowflake, HDFS, and object storage. • Deep understanding of streaming, batch processing, distributed computing, data modeling, and analytical architectures. Experience operating and evolving high-throughput, business-critical production systems. • Strong understanding of system scalability, reliability, observability, performance, and infrastructure cost optimization. • Strong stakeholder management and influencing skills, including experience working with senior technical and business leaders. • Demonstrated ability to navigate difficult conversations, resolve conflicting priorities, make trade-offs, and drive decisions. • Ability to connect business outcomes with technical strategy and engineering execution. • Experience with AdTech, high-scale real-time systems, audience platforms, ML/data infrastructure, or accelerated computing is a strong plus. Qualifications: • Bachelor's degree in Engineering (CS/IT) or an equivalent degree from a well-known institute or university; advanced degree is a plus but not required given depth of experience. What Success Looks Like Success means scaling and evolving an established, business-critical data ecosystem while building a strong engineering organization around it. The platform should become more reliable, scalable, observable, cost-efficient, and easier to operate, while continuing to support growing reporting, analytics, audience, ML, and AI use cases. At the same time, the Engineering Manager should develop a team with strong techn