Engineering Manager II - Analytics Platform

Spotify Spotify · Consumer · Toronto, ON · Platform

Engineering Manager II for Spotify's Learning Infrastructure Studio, focusing on foundational infrastructure and intelligence layer for data science and AI agents. The role involves leading a team, setting technical direction, driving delivery of high-impact initiatives, and staying hands-on with systems. Requires experience managing infrastructure/platform teams and understanding AI's impact on data workflows.

What you'd actually do

  1. Lead, grow, and support a team of engineers, fostering a culture of technical excellence, psychological safety, and continuous learning
  2. Set technical direction and partner closely with senior engineers to evolve our learning platforms
  3. Partner deeply with product managers and technical leads to shape the roadmap and ensure engineering perspective is present from strategy through to delivery.
  4. Drive delivery of high-impact initiatives, balancing speed, quality, and long-term sustainability
  5. Foster a strong engineering culture grounded in inclusion, ownership, and continuous improvement

Skills

Required

  • managing and developing engineering teams in infrastructure, platform, or data and analytics tooling context
  • led teams building infrastructure or platform capabilities used at scale
  • understanding of how AI is shifting data and analytics workflows
  • guiding engineers to transform AI-native tools for data scientists
  • balancing technical depth with people leadership and stakeholder collaboration
  • delivering complex technical projects with measurable impact
  • navigating ambiguity and helping teams prioritize effectively
  • operating as a player–coach, balancing leadership with hands-on technical contribution

Nice to have

  • technical excellence
  • psychological safety
  • continuous learning
  • inclusion
  • ownership
  • continuous improvement
  • coaching and mentoring engineers
  • technical and leadership skills
  • org-wide strategy for data science infrastructure and platform capabilities
  • evaluating emerging tooling
  • integrating new capabilities thoughtfully
  • building engineering practices ready for a world where humans and agents work together
  • participate in design reviews
  • guide architecture
  • maintain hands-on context to make high-quality technical decisions
  • technical multiplier
  • stepping in on ambiguous or high-stakes problems

What the JD emphasized

  • AI agents that need the right context, skills, and guardrails
  • AI is shifting data and analytics workflows
  • transform AI-native tools for data scientists

Other signals

  • powers data science work
  • AI agents that need the right context, skills, and guardrails
  • transform AI-native tools for data scientists