The opportunity
Pendo's Data Platform is the backbone of everything we do. Our systems process more than 50 billion events per day, peaking at over 1 million events per second, and manage dozens of petabytes of data across several production environments.
What you'll do
Lead multiple engineering teams responsible for our real-time data ingestion: pipeline, analytics batch job pipelines, and analytics query engine — setting technical direction and roadmap while balancing near-term delivery with long-term scalability, reliability, and cost efficiency.
Drive architectural decisions across storage, compute, and data modeling;: know when to adopt, build, or retire technologies and make those calls with conviction and clear rationale.
Own instrumentation standards across the core codebase: structured logging, OpenTelemetry metrics and tracing — ensuring teams across the organization can adopt them with minimal friction and that the resulting telemetry is useful, consistent, and cost-conscious.
Own the cost profile of the data platform at petabyte scale: forecast infrastructure spend for new product initiatives, improve cost attribution by customer, product, feature, and team, and diagnose and resolve cost overruns quickly.
Partner with SRE on shared on-call responsibilities, incident response,: postmortems, and reliability roadmaps; define and track SLOs for ingestion latency, query performance, and data correctness.
Serve as a technical authority in customer conversations on platform: performance, data integrity, and reliability — translating customer needs and escalations into clear engineering priorities.
What they're looking for
- Hire, coach, and develop engineering managers and senior engineers; build a: team culture grounded in high standards, clear communication, and continuous improvement.
- 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.