Engineering Manager, Data Platform

About [Employer]

Software runs the world and the pace is faster than ever. [Employer] helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building.

Trusted by 200,000+ organizations, [Employer] is today’s application monitoring standard and our team is building its AI-native future.

About the role

[Employer] helps over 4 million developers understand and resolve issues in their applications. Behind every error, performance trace, replay, and log is a large-scale data platform responsible for storing, querying, and analyzing massive volumes of telemetry data.

We're looking for an Engineering Manager to lead our Events Analytics Platform (EAP) team, which builds and operates the distributed data systems powering [Employer]'s products. The team manages petabytes of data and develops high-performance analytical infrastructure using technologies like ClickHouse, enabling fast, complex queries across billions of records.

As [Employer] grows, so do the technical challenges. The team is evolving its storage and query infrastructure to support increasing data volumes, more complex analytical workloads, and unified access to different types of telemetry data. EAP also provides the data foundation for Seer, [Employer]'s AI-powered debugging agent, enabling AI systems to access the production context needed to investigate and resolve issues.

In this role, you'll lead a team of engineers tackling complex distributed systems challenges while balancing architectural improvements with the reliability and performance of production systems. You'll bring strong technical judgment, guide engineering decisions, and work closely with product engineering teams to build capabilities that directly impact the developer experience.

This is an opportunity to lead a critical part of [Employer]'s infrastructure, solve challenging problems at scale, and shape how developers and AI-powered tools interact with application data.

What you'll do

• Lead and develop a team of engineers working on large-scale distributed data systems, fostering technical excellence, ownership, collaboration, and professional growth.
• Drive technical execution and architectural decisions across EAP's storage and query infrastructure, balancing scalability, reliability, performance, and long-term maintainability.
• Build and evolve analytical data systems supporting complex queries, high-throughput workloads, and efficient access to petabytes of event data.
• Guide the evolution of EAP's architecture, including query optimization, storage and compute separation, and the unification of different event types into a common analytical platform.
• Own the operational health of the platform, leading efforts to improve reliability, troubleshoot production issues, manage incidents, and address scaling challenges.
• Partner with product engineering teams to understand their technical requirements, make informed tradeoffs, and deliver platform capabilities that support new product experiences.
• Support [Employer]'s AI-powered capabilities by ensuring the platform efficiently serves the analytical workloads and production context required by Seer and other AI-driven experiences.
• Set clear priorities and drive projects from design through production, helping the team execute effectively while managing competing technical and product needs.

You'll love this job if you

• Enjoy leading engineers while staying closely involved in technical discussions, architecture, and complex problem-solving.
• Get excited about building and operating large-scale distributed systems where performance, reliability, and efficiency matter.
• Have a strong interest in analytical databases, storage engines, query systems, and the engineering challenges behind processing massive volumes of data.
• Like working through difficult technical tradeoffs and turning ambiguous requirements into practical engineering solutions.
• Take pride in building reliable production systems, understanding how they behave under load, and continuously improving their performance.
• Enjoy collaborating with product engineering teams and understanding how infrastructure decisions influence the end-user experience.
• Want to lead a team whose work directly supports [Employer]'s core products and growing AI-powered capabilities.

Qualifications

• Significant professional software engineering experience, including experience managing and developing engineers, leading technical projects, and driving engineering execution.
• Strong technical background in distributed systems, data infrastructure, analytical databases, or large-scale backend systems.
• Hands-on experience building, scaling, or operating complex data-intensive systems, with depth in at least one of the following:Analytical database infrastructure: Operating, scaling, optimizing, or maintaining OLAP systems such as ClickHouse, Apache Pinot, Apache Druid, or similar technologies in production.
• Complex distributed data platforms: Designing and building sophisticated systems using analytical databases or distributed storage technologies as foundational components, with meaningful ownership of the underlying architecture, performance, and reliability.

Experience making architectural decisions and technical tradeoffs involving system scalability, performance, reliability, or operational complexity.

Demonstrated ownership of production systems, including troubleshooting, incident response, performance optimization, and operational improvements.

Experience leading engineering projects from technical design through implementation, deployment, and ongoing operations.

Strong communication and collaboration skills, including the ability to work with cross-functional stakeholders and translate technical requirements into actionable engineering priorities.

Bonus points for:

• Experience with ClickHouse, Pinot, Druid, or other column-oriented analytical databases.
• Experience with query optimization, analytical storage architecture, or high-throughput data processing.
• Experience building infrastructure for observability, telemetry, developer tools, or event-driven applications.
• Experience working closely with product engineering teams to develop infrastructure supporting customer-facing features.
• Proficiency with Python, Rust, or similar backend programming languages.

Not sure if you meet 100% of the qualifications? We encourage you to apply anyway. We're interested in people who are excited about this opportunity and eager to grow.

The base salary range that [Employer] reasonably expects to pay for this position is $220,000 to $400,000 USD, plus an annual performance bonus of up to 15% of base salary. A successful candidate’s actual base salary (or hourly wage) amount will be determined by a variety of relevant factors including, without limitation, the candidate’s work location, education, work and other relevant experience, skills, and job-related knowledge. A successful candidate will be eligible to participate in [Employer]’s employee benefit plans/programs applicable to the candidate’s position (including incentive compensation, equity grants, paid time off, and group health insurance coverage). See [Employer] Benefits for more details about the Company’s benefit plans/programs.

Equal Opportunity at [Employer]

[Employer] is committed to providing equal employment opportunities to its employees and candidates for employment regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other legally-protected characteristic. This commitment includes the provision of reasonable accommodations to employees and candidates for employment with physical or mental disabilities who require such accommodations in order to (a) perform the essential functions of their jobs, or (b) seek employment with [Employer]. We strive to build a diverse team, with an inclusive culture where every teammate can thrive. [Employer] is an open-source company because we believe that everyone, everywhere, should have the ability and tools to make great software. Software should be accessible. That starts with making our industry accessible.

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