As a part of LinkedIn’s Data Science team, you will leverage big data to drive business decisions and deliver data-driven insights, metrics, and tools. With over 800 million members worldwide and a focus on user experience, LinkedIn provides ample opportunities for data engineers to make a significant impact and advance their careers. We are seeking a talented and driven individual to join our data-centric culture, working closely with cross-functional teams to develop infrastructure and deliver tools or data structures that enable data-driven decision-making.
Responsibilities
- Collaborate with high-performing data science professionals and cross-functional teams to identify business opportunities and build scalable data solutions.
- Act as an owner for the company, managing complex data systems for products or groups of products.
- Perform necessary data transformations to support products that empower data-driven decision-making.
- Build and manage data pipelines, design databases, and establish efficient design and programming patterns.
- Design, implement, integrate, and document performant systems or components for data flows or applications at scale.
- Ensure best practices and standards in the data ecosystem are shared across teams.
- Understand analytical objectives, make logical recommendations, and drive informed actions.
- Engage with internal platform teams to prototype and validate in-house tools for deriving insights from large datasets or automating complex algorithms.
- Drive projects to completion with minimal guidance and contribute to engineering innovations that align with LinkedIn’s vision and mission.
Basic Qualifications
- Bachelor's Degree in a quantitative discipline such as Computer Science, Statistics, Operations Research, or similar.
- 5+ years of industry or relevant academic experience working with large datasets.
- Proficiency in SQL/Relational databases.
- Background in at least one programming language (e.g., R, Python, Java, Scala, PHP).
Preferred Qualifications
- Advanced degree (MS or PhD) in a quantitative discipline.
- Experience with data pipelines using Spark and Hive.
- Knowledge of data modeling, ETL concepts, and patterns for efficient data governance.
- Familiarity with manipulating massive-scale structured and unstructured data.
- Experience with distributed data systems such as Hadoop and related technologies.
- Proficiency in data visualization tools like Tableau, R visualization packages, D3, etc.
- Understanding of Unix-like systems, git, and review board.
Suggested Skills
- Java
- Data Pipeline
- ETL
- Data Manipulation
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