Principal Staff Software Engineer, Data Infrastructure

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. 

LinkedIn’s Data Infrastructure organization builds and operates the foundational platforms that store, process, and serve data across LinkedIn. The organization spans online relational databases, document stores, and key-value serving systems, as well as offline analytical, streaming, batch processing, and orchestration platforms. These systems power LinkedIn’s products and support the growing demands of AI applications at global scale.

We are looking for a Principal Staff Software Engineer with deep expertise in database internals, relational database performance, and large-scale distributed systems. This role will lead database performance and efficiency optimization, with an initial emphasis on relational systems and an opportunity to extend that work across online and offline data platforms.

You will work on query planning and execution, indexing, storage efficiency, and application access patterns to improve performance and make better use of LinkedIn’s infrastructure. The ideal candidate brings a deep understanding of online transactional databases and can apply that expertise to offline analytical systems and the convergence of these architectures, including query federation and hybrid transactional and analytical processing (HTAP).

This is a highly technical leadership role for a hands-on builder who can set direction and drive implementation across multiple teams. You will partner closely with database, data platform, and application engineers, engage directly with code and design decisions, and turn optimization opportunities into production improvements with company-level impact.

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 drive improvements in 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 that connect 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.

Department: Engineering
Function: Sales
Experience Level: Director