Job description

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. Basic 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 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. Preferred Qualifications • 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. • A 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. Suggested Skills • Database Systems • Database Performance Optimization • Distributed Systems • Technical Strategy LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $231,000 to $378,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit careers.linkedin.com . Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful. If you need a Reasonable Accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us and describe the specific Accommodation requested for a disability-related limitation. Fill out an Accommodation request here: app.smartsheet.com Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to: • Documents in alternate formats or read aloud to you • Having interviews in an accessible location • Being accompanied by a service dog • Having a sign language interpreter present for the interview A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response. LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information. San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records. Pay Transparency Policy Statement As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: lnkd.in. Global Data Privacy Notice and Compliance Posters for Job Candidates Please use this link to access documents that provide information about how LinkedIn handles the personal data of employees and job applicants, as well as the E-Verify Participation Notice and the Department of Justice Immigrant and Employee Rights Section Right to Work posters: linkedin.com.

Requirements

Must-haves

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.
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.

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.
A 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.
Suggested Skills
Database Systems
Database Performance Optimization
Distributed Systems

Application prep

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Keywords to mirror

Platform: smartrecruiters
Effort: medium

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