Principal Staff Software Engineer, Data Infrastructure

LinkedIn•Mountain View, CA

1d•Hybrid

About The Position

LinkedIn’s Data Infrastructure organization builds and operates foundational platforms that store, process, and serve data across the company. These systems include online relational databases, document stores, key-value serving systems, and offline analytical, streaming, batch-processing, and orchestration platforms that support LinkedIn products and AI applications at global scale. The Principal Staff Software Engineer will lead database performance and efficiency optimization, initially emphasizing relational systems, with opportunities to extend that work across online and offline data platforms. The role focuses on query planning and execution, indexing, storage efficiency, and application access patterns. The engineer will help connect online transactional databases with offline analytical systems, including query federation and hybrid transactional and analytical processing (HTAP). This is a hands-on technical leadership role with company-level impact. The engineer will set technical direction, work directly with code and design decisions, and partner with database, data-platform, and application engineers to turn optimization opportunities into production improvements. The role is hybrid, performed both from home and from a LinkedIn office on select days as determined by business needs.

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 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 database 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.
Stay hands-on through code and design reviews, performance investigations, and proofs of concept that validate technical approaches.
Make architectural tradeoffs involving 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 for emerging AI workloads.
Lead cross-team initiatives 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.