Staff Software Engineer, Data Engineering

LinkedIn is the world’s largest professional network, built to help members of all backgrounds and experiences achieve more in their careers. Our vision is to create economic opportunity for every member of the global workforce. Every day our members use our products to make connections, discover opportunities, build skills and gain insights. We believe amazing things happen when we work together in an environment where everyone feels a true sense of belonging, and that what matters most in a candidate is having the skills needed to succeed. It inspires us to invest in our talent and support career growth. Join us to challenge yourself with work that matters.

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 Science team leverages big data to empower business decisions and deliver data-driven insights, metrics, and tools in order to drive member engagement, business growth, and monetisation efforts. With over 800 million members around the world, a focus on great user experience, and a mix of B2B and B2C programs, LinkedIn offers countless ways for an ambitious data engineer to have an impact and transform your career.

We are now looking for a talented and driven individual to accelerate our efforts and be a major part of our data-centric culture. This person will work closely with various cross-functional teams such as product, marketing, sales, engineering, and operations to develop infrastructure and deliver tools or data structures that enable data-driven decision-making. Successful candidates will exhibit technical acumen and business savviness with a passion for making an impact by enabling both producers and consumers of data insight to work smarter.

Responsibilities:
- Work with a team of high-performing data science professionals, and cross-functional teams to identify business opportunities and build scalable data solutions.
- Build data expertise, act like an owner for the company and manage complex data systems for a product or a group of products.
- Perform all of the necessary data transformations to serve products that empower data-driven decision making.
- Establish efficient design and programming patterns for engineers as well as for non-technical partners.
- Design, implement, integrate and document performant systems or components for data flows or applications that power analysis at a massive scale.
- Ensure best practices and standards in our data ecosystem are shared across teams.
- Understand the analytical objectives to make logical recommendations and drive informed actions.
- Engage with internal data platform teams to prototype and validate tools developed in-house to derive insight from very large datasets or automate complex algorithms.
- Contribute to engineering innovations that fuel LinkedIn’s vision and mission.

Basic Qualifications:
- Bachelor's Degree in a quantitative discipline: Computer Science, Statistics, Operations Research, Informatics, Engineering, Applied Mathematics, Economics, etc
- 8+ years of relevant industry or relevant academia experience working with large amounts of data
- Experience with SQL/Relational databases
- Background in at least one programming languages (e.g., R, Python, Java, Scala, PHP, JavaScript)

Preferred Qualifications:
- BS and 10+ years of relevant work experience, MS and 9+ years of relevant work experience, or Ph.D.
- Experience in developing data pipelines using Spark and Hive.
- Experience with data modeling, ETL (Extraction, Transformation & Load) concepts, and patterns
for efficient data governance.
- Experience in either the front-end or back-end development of data-powered applications.
- Deep understanding of technical and functional designs for relational and MPP Databases,
- Reporting and Data Mining systems.
- Experience working in the product, sales, or marketing analytics domains.
- Experience in data visualisation and dashboard design including tools such as Tableau, R
visualisation packages, D3, and other JavaScript libraries, etc.
- Published work in academic conferences or industry circles

Suggested Skills :
Data Pipeline
Data Engineering
Data Manipulation

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