Job Description
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, we trust each other to do our best work where it works best for us and our teams. This role offers a hybrid work option, meaning you can both work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together.
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 monetization 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.
- Build and manage data pipelines, design and architect databases.
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 platform teams to prototype and validate tools developed in-house to derive insight from very large datasets or automate complex algorithms.
- Be a self-starter, Initiate and drive projects to completion with minimal guidance.
- 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.
- 2+ 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)
Preferred Qualifications:
- Experience working with large amounts of data
- MS or PhD in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
- 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 with manipulating massive-scale structured and unstructured data.
- Experience with distributed data systems such as Hadoop and related technologies (Spark, Presto, Pig, Hive, etc.).
- Experience with either data workflows/modeling, front-end engineering, or back-end engineering.
- Deep understanding of technical and functional designs for relational and MPP Databases
- Experience in data visualization and dashboard design including tools such as Tableau, R visualization packages, D3, and other Javascript libraries, etc.
- Knowledge of Unix and Unix-like systems, git and review board.
Suggested Skills:
Java
Data Pipeline
ETL
Data Manipulation
India Disability Policy
LinkedIn is an equal employment opportunity employer offering opportunities to all job seekers, including individuals with disabilities. For more information on our equal opportunity policy, please visit https://legal.linkedin.com/content/dam/legal/Policy_India_EqualOppPWD_+-12-2026.pdf
Global Data Privacy Notice for Job Candidates
This document provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal
At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers a hybrid work option, meaning you can both work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together.
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 monetization 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.
- Build and manage data pipelines, design and architect databases.
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 platform teams to prototype and validate tools developed in-house to derive insight from very large datasets or automate complex algorithms.
- Be a self-starter, Initiate and drive projects to completion with minimal guidance.
- 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.
- 2+ 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)
Preferred Qualifications:
- Experience working with large amounts of data
- MS or PhD in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
- 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 with manipulating massive-scale structured and unstructured data.
- Experience with distributed data systems such as Hadoop and related technologies (Spark, Presto, Pig, Hive, etc.).
- Experience with either data workflows/modeling, front-end engineering, or back-end engineering.
- Deep understanding of technical and functional designs for relational and MPP Databases
- Experience in data visualization and dashboard design including tools such as Tableau, R visualization packages, D3, and other Javascript libraries, etc.
- Knowledge of Unix and Unix-like systems, git and review board.
Suggested Skills:
Java
Data Pipeline
ETL
Data Manipulation
India Disability Policy
LinkedIn is an equal employment opportunity employer offering opportunities to all job seekers, including individuals with disabilities. For more information on our equal opportunity policy, please visit https://legal.linkedin.com/content/dam/legal/Policy_India_EqualOppPWD_+-12-2026.pdf
Global Data Privacy Notice for Job Candidates
This document provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal
💡 Quick Summary
Seeking a career-building opportunity? The Software Engineer| Data Science position is now open for candidates interested in the IT Engineer & Developer Jobs sector. This role in Bangalore offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in IT Engineer & Developer Jobs is a plus.
