Staff Data Engineer - Data Science
Description For Staff Data Engineer - Data Science
LinkedIn, the world's largest professional network with over 1 billion members globally, is seeking a Staff Data Engineer to join their Data Science team. This role represents an exceptional opportunity to work at the intersection of big data and business impact, helping shape the future of professional networking and career development.
The position offers a hybrid work arrangement, allowing flexibility between remote work and office presence at LinkedIn's Sunnyvale location. As a Staff Data Engineer, you'll be instrumental in leveraging big data to empower business decisions and deliver data-driven insights, metrics, and tools that drive member engagement, business growth, and monetization efforts.
Your role will involve collaborating with cross-functional teams including product, marketing, sales, engineering, and operations to develop infrastructure and deliver tools that enable data-driven decision-making. You'll be working with massive datasets from LinkedIn's billion-plus member base, building scalable solutions that impact how professionals worldwide connect and grow their careers.
Key responsibilities include designing and implementing performant systems for data flows, establishing efficient design patterns, and ensuring best practices across the data ecosystem. You'll need to combine technical expertise with business acumen to transform complex data into actionable insights that drive LinkedIn's strategic initiatives.
The ideal candidate brings a strong foundation in data engineering, with experience in SQL, programming languages like Python or Java, and working with large-scale data systems. You should have at least 7 years of relevant experience with a Bachelor's degree, or equivalent combinations of education and experience.
LinkedIn offers a comprehensive benefits package including generous health and wellness programs, equity compensation, and performance bonuses. The compensation range of $147,000 to $240,000 reflects the senior level of this position and LinkedIn's commitment to attracting top talent.
This role presents an exciting opportunity to work with cutting-edge data technologies while making a significant impact on how professionals worldwide connect, learn, and advance their careers. Join LinkedIn's data science team to be part of shaping the future of professional networking while working alongside talented individuals in a collaborative, innovative environment.
Responsibilities For Staff Data Engineer - Data Science
- Work with high-performing data science professionals and cross-functional teams to identify business opportunities and build scalable data solutions
- Perform data transformations to serve products that empower data-driven decision making
- Establish efficient design and programming patterns for engineers and non-technical partners
- Design, implement, integrate and document performant systems for data flows at massive scale
- Ensure best practices and standards in data ecosystem are shared across teams
- Understand analytical objectives to make logical recommendations
- Engage with internal data platform teams to prototype and validate tools
- Contribute to engineering innovations that fuel LinkedIn's vision and mission
Requirements For Staff Data Engineer - Data Science
- Bachelor's Degree in a quantitative discipline (Computer Science, Statistics, Operations Research, etc)
- 4+ years of relevant industry or academia experience working with large amounts of data
- Experience with SQL/Relational databases
- Background in at least one programming language (R, Python, Java, Scala, PHP, JavaScript)
- BS and 7+ years of relevant work experience, MS and 5+ years of relevant work experience, or Ph.D. and 3+ years of relevant work/academia experience
Benefits For Staff Data Engineer - Data Science
- Generous health and wellness programs
- Time away for employees of all levels
- Annual performance bonus
- Stock options
- Comprehensive benefits package
Jobs Related To LinkedIn Staff Data Engineer - Data Science
