Principal Engineer, Data Platform

New

Based in the United StatesFull-TimePrincipal

SalaryBase salary: $246,000–$339,000 USD, depending on experience, expertise, and other relevant factors. Potential performance-based bonus. Equity opportunities.

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Job Details

Required Skills: AWSPostgreSQLKubernetesMongoDBClickhouseRedisDistributed Systems

Requirements

Bring extensive hands-on experience designing, building, and operating data platforms and large-scale storage systems.
Have production experience with MongoDB, ClickHouse, Redis, and PostgreSQL.
Have a strong understanding of cloud-native data storage, including managed data services on AWS.
Have advanced experience with Kubernetes, containers, and distributed systems.
Demonstrate success designing and executing large-scale data migrations, particularly live migrations.
Have experience with cell-based, multi-tenant, or multi-region architectures.
Have a background building internal platforms and developer tooling used by engineering teams.
Be comfortable with teams owning and operating the systems they build.
Demonstrate the ability to deliver significant technical changes across codebases owned by other teams.
Be able to anticipate and proactively address data scalability and reliability constraints.
Bring technical leadership, communication, and collaboration skills to influence stakeholders and drive technical roadmaps.
Nice to have: experience operating storage systems on Kubernetes, familiarity with Crossplane control planes, or experience supporting private, isolated, on-premises, or air-gapped deployments.

Responsibilities

Define the architecture, technical standards, and long-term roadmap for the Data Platform and its storage ecosystem.
Design and build capabilities and tools for large-scale data migrations with minimal downtime.
Develop self-service platforms, shared libraries, and tooling for product teams to provision, operate, and scale storage systems.
Lead data-layer changes for cell-based architecture, including data placement, tenant routing, replication, and isolation.
Establish and implement a managed-first strategy for data services and systems.
Identify and address scalability, performance, and reliability limitations across data stores, regions, and cells.
Partner with Cloud Platform, Site Reliability Engineering, and Developer Experience teams.
Influence architectural decisions, raise engineering standards, and mentor Staff and Senior Engineers.
Apply AI-powered tools to platform engineering workflows, including slow-query analysis and incident diagnosis.

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