Principal Staff Software Engineer, Data Infrastructure

LinkedIn•Mountain View, CA

2d•$231,000 - $378,000•Hybrid

About The Position

LinkedIn’s Data Infrastructure organization builds and operates foundational platforms for storing, processing, and serving data across online relational databases, document stores, key-value systems, analytical platforms, streaming, batch processing, and orchestration. These systems power LinkedIn products and support AI applications at global scale. The Principal Staff Software Engineer will lead database performance and efficiency optimization, initially focusing on relational systems and potentially extending across online and offline data platforms. The role covers query planning and execution, indexing, storage efficiency, and application access patterns, with an emphasis on applying online transactional database expertise to analytical systems and converged architectures such as query federation and HTAP. This is a hands-on technical leadership position. The engineer will set direction and drive implementation across teams, engage in code and design decisions, partner with database, platform, and application engineers, and deliver production improvements with company-level impact.

Requirements

BA/BS degree in Computer Science or a related technical field, or equivalent practical experience.
10+ years of industry experience in software engineering, database systems, distributed systems, or related infrastructure areas.
5+ years of experience in an architect, technical lead, or senior technical leadership capacity, driving architecture and technical direction across complex systems or multiple engineering teams.
Experience designing, building, or optimizing production database systems, including relational databases, query execution, indexing, or storage components.
Experience diagnosing and improving database or distributed system performance through changes to query execution, storage, or application access patterns.
Hands-on programming experience and experience leading technically complex initiatives across team or organizational boundaries.

Nice To Haves

Master’s or PhD in Computer Science or a related technical field, with experience operating at Principal Staff or equivalent scope.
Deep expertise in database internals, including query optimizers, execution engines, indexing, storage engines, and consistency and availability tradeoffs.
Experience spanning online transactional databases and offline analytical systems, including query federation, HTAP, or relational interfaces over data lake storage.
Track record of delivering measurable improvements in performance, hardware utilization, or infrastructure cost across large production data platforms.
Experience building or optimizing relational and distributed database platforms such as MySQL, PostgreSQL, TiDB, or comparable systems.
Experience with the internals or performance optimization of analytical, streaming, or batch processing platforms such as Spark, Flink, or comparable systems.
Understanding of the performance and storage implications of AI-oriented workloads, including vector embeddings, text search, and inference-related operations.
Demonstrated ability to set technical strategy, align senior engineers and leaders, and drive implementation and adoption across multiple teams while remaining hands-on.

Responsibilities

Set technical direction for database performance and efficiency optimization across LinkedIn’s online and offline data infrastructure.
Lead improvements to databases and offline systems, including query planning and execution, indexing, storage, and application access patterns.
Diagnose performance bottlenecks across application, database, and storage layers and improve latency, throughput, resource utilization, and infrastructure cost.
Extend optimization approaches across offline analytical, nearline, streaming, and batch systems, identifying opportunities to improve query execution and storage efficiency.
Shape architectures connecting online transactional and offline analytical systems, including query federation and converged data platforms.
Remain hands-on through code and design reviews, performance investigations, and proofs of concept that validate technical approaches.
Make architectural tradeoffs across performance, consistency, availability, reliability, scalability, and cost.
Partner with platform and application teams to translate workload requirements into efficient data access and execution patterns, including emerging AI workloads.
Lead initiatives across teams from problem identification and design through implementation, production rollout, and adoption.
Mentor senior engineers and strengthen engineering practices for database design, performance analysis, and optimization.