Principal Data Infrastructure Architect

Mountain View (CA)

Hybrid

USD 231,000 - 378,000

Full time

8 days ago

Get a reply from this employer

Your resume and cover letter, tailored to exactly what they’re hiring for.

Get past ATS filters

Job summary

LinkedIn is seeking a Principal Staff Software Engineer to drive database performance and efficiency across online and offline data infrastructure in a hybrid work model in Mountain View, CA. You will lead query planning, indexing, and storage optimizations while mentoring engineers and shaping architectures that unite transactional and analytical systems.

The role emphasizes deep expertise in relational databases, distributed systems, and cost-conscious scalability, with hands-on code

Qualifications

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 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.

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.