Director of Engineering, Data Platform

Description

The company's data platform is the core of its operations, handling more than 50 billion events a day, with peaks above 1 million events per second and dozens of petabytes of data across several production environments.

The platform covers real-time ingestion, batch processing and analytics at scale, plus the instrumentation standards that make the whole codebase observable.

It is written mainly in Go and runs on Google Kubernetes Engine, using Cloud Pub/Sub, Firestore, Google Cloud Storage, Redis and memcache. The stack is expected to change, and you will help decide how it changes.

The company is hiring a Director of Engineering to lead the teams that own this infrastructure. The role is a leadership position that still requires deep technical skill: you will set strategy, build and grow strong engineering teams, and work closely with Site Reliability Engineering.

You will stay close enough to the code to review designs, debug production issues and step in when needed. You will also own the cost of running the platform at this scale and act as a trusted technical voice for customers on performance, data integrity and reliability. Architecture choices here have immediate business impact and a high technical bar.

This role is based in Raleigh, NC, on a hybrid schedule with 3 days per week in the office.

Responsibilities

• Lead several engineering teams that own the real-time ingestion pipeline, the batch analytics pipelines and the analytics query engine. Set technical direction and roadmap, balancing delivery now against scalability, reliability and cost later.
• Drive architecture decisions across storage, compute and data modeling. Decide when to adopt, build or retire a technology, and justify those calls clearly.
• Own the instrumentation standards across the core codebase, including structured logging and OpenTelemetry metrics and tracing. Make sure teams can adopt them easily and that the telemetry stays 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 quickly diagnose and fix cost overruns.
• Share on-call duties, incident response, postmortems and reliability roadmaps with SRE. Define and track SLOs for ingestion latency, query performance and data correctness.
• Act as the technical authority in customer conversations about performance, data integrity and reliability. Turn customer needs and escalations into clear engineering priorities.
• Hire, coach and develop engineering managers and senior engineers. Build a team culture based on high standards, clear communication and continuous improvement.

Requirements

• You build things rather than maintain them. You do well where no clear path exists yet and you define it yourself. You look for gaps, shape solutions and push them forward without waiting for direction.
• You make cost and reliability tradeoffs visible early, before they become crises, and you grow teams that can work at the same level of ambition.
• You use AI tools as a core part of how you work, not occasionally. You judge new tools on their merits, separate real shifts from hype, and guide your teams in adopting AI-assisted and agentic coding in ways that raise quality without adding risk.
• Several years of engineering management experience, including managing managers or several teams, with a record of building high-performing teams in a fast-growing SaaS company.
• Experience building and running large, data-intensive distributed systems in production, ideally at a scale comparable to 50B+ events per day and petabyte-level data.
• Strong experience with real-time ingestion, batch processing and analytics or query systems.
• Proven cost and financial skill at infrastructure scale, including forecasting, optimization, attribution and overrun diagnosis. You can build a credible cost model and explain its assumptions to technical and financial stakeholders.
• Deep experience with relational (SQL), document (NoSQL), columnar and vector databases. Hands-on Firestore experience or similar is a strong plus.
• Hands-on work with more than one cloud platform (GCP, AWS or Azure), including object storage and managed data services. Deep GCP experience is especially valuable.
• Experience with message queues and streaming systems such as Pub/Sub, Kafka or SQS, caching tools such as Redis, Valkey or memcache, and containerized workloads on Kubernetes at scale.
• Working knowledge of observability practices and tools, ideally including OpenTelemetry. You can read, review and contribute to Go code.
• You can explain complex topics such as performance tradeoffs, consistency guarantees and reliability commitments to customers, executives and engineers. You make sound, pragmatic decisions in ambiguous situations.
• Nice to have: experience with high-cardinality, high-volume event or telemetry data.
• Nice to have: background in multi-tenant SaaS platforms.
• Nice to have: familiarity with FinOps practices and cloud billing data, including cost allocation through tagging, labeling and usage-based attribution.
• Nice to have: contributions to open-source projects in the data or observability space.

Conditions

Based in Raleigh, NC, on a hybrid model with 3 days per week in the office.

Salary range for this role in North Carolina is USD $235,000 to USD $280,000.

The company is an equal opportunity employer and welcomes people of all backgrounds, experiences, abilities and perspectives.

Benefits

• Highly competitive coverage with employer-paid premiums, including $0 premium options.
• Strong 401(k) match.
• Equity.
• Flexible time off.
• Salary is one part of a wider set of pay and reward options.