Director of Engineering, Data Platform

Required

• Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.
• You're a builder, not a maintainer.
• You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great engineering directors don't just manage delivery — they operate as technical owners, setting architectural direction, making cost and reliability tradeoffs visible before they become crises, and building teams that can operate at the same level of ambition.
• You're AI-curious — genuinely.
• You're not using AI tools occasionally. You're rewiring how you work around them — and you bring that same orientation to your teams. You evaluate emerging tools with sound judgment, separate meaningful shifts from hype, and guide the adoption of AI-assisted and agentic coding practices across the organization in a way that raises the bar rather than creates new risk.
• Several years of engineering management experience, including managing managers or multiple teams; a track record of building high-performing teams in a high-growth SaaS environment.
• Demonstrated experience building and operating large-scale, data-intensive distributed systems in production — ideally at a scale comparable to 50B+ events per day and petabyte-level data management.
• Strong experience across real-time data ingestion, batch processing, and analytics or query systems.
• Demonstrated cost and financial acumen at infrastructure scale: forecasting, optimization, attribution, and overrun diagnosis; ability to build a credible cost model and communicate its assumptions to both technical and financial stakeholders.
• Deep experience with diverse database technologies — relational (SQL), document (NoSQL), columnar, and vector; hands-on experience with Firestore or similar is a strong plus.
• Hands-on experience with multiple cloud platforms (GCP, AWS, Azure), including object storage and managed data services; deep GCP experience is especially valuable.
• Experience with message queuing and streaming systems (Pub/Sub, Kafka, SQS, or similar), caching technologies (Redis, Valkey, memcache), and containerized workloads on Kubernetes at scale.