Director of Engineering, Data Platform

Pendo•Raleigh, NC

2d•$235,000 - $280,000•Hybrid

About The Position

Pendo is hiring a Director of Engineering to lead the teams responsible for its Data Platform, which processes more than 50 billion events per day, peaks at over 1 million events per second, and manages dozens of petabytes of data across production environments. The platform supports real-time ingestion, batch processing, analytics, and organization-wide instrumentation standards. It is built primarily in Go and runs on Google Kubernetes Engine, using services and technologies including Cloud Pub/Sub, Firestore, Google Cloud Storage, Redis, and memcache. This is a technically hands-on leadership role. The Director will set strategy, build and grow engineering teams, partner closely with Site Reliability Engineering, and stay involved in design reviews, production debugging, and code contributions when needed. The role also owns the platform's operating economics and serves as a technical voice with customers on performance, data integrity, and reliability. The role is based in Raleigh, North Carolina, and follows Pendo's hybrid model, with three days per week in the office.

Requirements

Several years of engineering management experience, including managing managers or multiple teams.
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.
Strong experience with real-time data ingestion, batch processing, and analytics or query systems.
Infrastructure-scale cost and financial expertise, including forecasting, optimization, attribution, and overrun diagnosis; ability to develop and communicate a credible cost model.
Deep experience with relational (SQL), document (NoSQL), columnar, and vector database technologies.
Hands-on experience with multiple cloud platforms, including object storage and managed data services.
Experience with message queuing and streaming systems, caching technologies, and containerized workloads on Kubernetes at scale.
Working knowledge of observability practices and tooling, ideally including OpenTelemetry.
Comfort reading, reviewing, and contributing to Go code.
Ability to communicate credibly with customers, executives, and engineers about complex topics, and to make pragmatic, well-reasoned decisions in ambiguous situations.
Genuine interest in evaluating and adopting AI tools and practices with sound judgment.

Nice To Haves

Experience building or operating systems at a scale comparable to 50 billion or more events per day and petabyte-level data management.
Deep GCP experience; experience with Firestore or similar is a strong plus.
Experience with high-cardinality, high-volume event or telemetry data.
Background in multi-tenant SaaS platforms.
Familiarity with FinOps practices and cloud billing data, including tagging, labeling, and usage-based cost attribution.
Contributions to open-source projects in the data or observability ecosystem.

Responsibilities

Lead multiple teams responsible for real-time data ingestion, analytics batch pipelines, and the analytics query engine; set technical direction and roadmaps while balancing delivery, scalability, reliability, and cost efficiency.
Drive architectural decisions across storage, compute, and data modeling, including decisions to adopt, build, or retire technologies.
Own instrumentation standards across the core codebase, including structured logging, OpenTelemetry metrics, and tracing; enable broad adoption and ensure telemetry is useful, consistent, and cost-conscious.
Own the data platform's cost profile at petabyte scale, forecast infrastructure spend for product initiatives, improve cost attribution, and diagnose and resolve cost overruns.
Partner with SRE on 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 about platform performance, data integrity, and reliability, translating customer needs and escalations into engineering priorities.
Hire, coach, and develop engineering managers and senior engineers, building a culture of high standards, clear communication, and continuous improvement.
Guide the evaluation and adoption of AI-assisted and agentic coding practices across the organization.