Director of Engineering, Data Platform

Pendo•Raleigh, NC

1d•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 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. The director will help shape how the platform and its technology evolve. This is a technically hands-on leadership role involving strategy, team development, architecture, and close partnership with Site Reliability Engineering. The director will set technical direction, review designs, help debug production issues, and contribute when needed. They will also own the platform’s infrastructure economics and represent engineering in customer discussions about performance, data integrity, and reliability. The role is based in Raleigh, North Carolina, under 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.
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 50 billion or more events per day and petabyte-level data management.
Strong experience with real-time data ingestion, batch processing, and analytics or query systems.
Infrastructure-scale cost and financial acumen, including forecasting, optimization, attribution, and diagnosing overruns; ability to build a credible cost model and communicate its assumptions to technical and financial stakeholders.
Deep experience with diverse database technologies, including relational (SQL), document (NoSQL), columnar, and vector databases.
Hands-on experience with multiple cloud platforms, including GCP, AWS, or Azure, and with object storage and managed data services.
Experience with message queuing and streaming systems such as Pub/Sub, Kafka, or SQS; caching technologies such as Redis, Valkey, or memcache; 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 such as performance tradeoffs, consistency guarantees, and reliability commitments.
Sound judgment and ability to make pragmatic, well-reasoned decisions in ambiguous situations.
Genuine interest in AI tools and practices, with the ability to evaluate emerging tools and guide thoughtful adoption of AI-assisted and agentic coding.

Nice To Haves

Hands-on experience with Firestore or similar technologies.
Deep GCP experience.
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 engineering teams responsible for real-time data ingestion, analytics batch pipelines, and the analytics query engine; set technical direction and roadmap 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; make adoption easy 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 by customer, product, feature, and team, and diagnose and resolve cost overruns.
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 about platform performance, data integrity, and reliability, translating customer needs and escalations into engineering priorities.
Hire, coach, and develop engineering managers and senior engineers, and build a team culture grounded in high standards, clear communication, and continuous improvement.
Guide adoption of AI-assisted and agentic coding practices across the organization, evaluating emerging tools and managing risks.