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Table of contents
1.
Introduction
2.
About LinkedIn
3.
Salary and Perks
4.
About the role
5.
Required Skill Set
5.1.
Basic Qualifications
5.2.
Preferred Qualifications
6.
Responsibilities of Staff Data Engineer at LinkedIn
7.
Preparation Strategy
8.
Resources
9.
Career Path
9.1.
RoadMap for preparation
9.1.1.
A strong background in mathematics and computer science
9.1.2.
Experience working with large amounts of data
9.1.3.
Experience with machine learning and statistical modeling
9.1.4.
A willingness to learn
10.
Frequently Asked Questions
10.1.
What recruiters search in a data engineer?
10.2.
What skills are required to be a data engineer?
10.3.
What does a data engineer do?
10.4.
What qualities must a data engineer possess?
10.5.
Who works together with a data engineer?
11.
Conclusion
Last Updated: Mar 27, 2024

Staff Data Engineer at LinkedIn

Author Manan Singhal
0 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

We all have heard of LinkedIn, and most of us have LinkedIn accounts, but what if you want to join LinkedIn as a staff data engineer? Here comes Coding Ninjas with this blog so that you achieve your goals.

This blog will provide info on what needs to be done to become a staff data engineer at LinkedIn.

LinkedIn

Check out our articles on Ways to join LinkedIn at different positions opened by LinkedIn.

So, let's get started.

About LinkedIn

LinkedIn is the world's largest professional network company founded on December 28, 2002, but officially launched on May 5, 2003, having its headquarters in Sunnyvale, California. It is a social networking service that runs on mobile applications and websites. LinkedIn's objective is to connect professionals worldwide to increase their productivity and success, and its vision is to promote economic opportunity for every member of the global workforce.

LinkedIn

Users can connect with people they've worked with on LinkedIn, display their professional backgrounds and talents, hunt for employment opportunities, and find employees.

With Microsoft's completion of the LinkedIn acquisition in December 2016, the world's top professional cloud and leading professional network were combined.

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Salary and Perks

This section will look at the salary of staff data engineers at LinkedIn gets.

Salary

Designation

Average Base Salary

Estimated Take-Home Salary

Staff Data Engineer

$131,723

$205,636

 

* The salaries mentioned above are subject to change.

LinkedIn offers a range of perks to its employees. Let us look at some of those benefits:

Benefits

 

‍🔧 Training and Development: They provide several appealing training and development possibilities, such as various training, seminars, workshops, a virtual English course, and access to the SAP Learning Hub.

‍🔧 Attractive Employee Discounts: On their corporate benefits employee site, you'll find various appealing employee discounts in multiple categories.

‍🔧 Health and Safety Benefits: Whether direct or occupational disability insurance, the company provides these products at competitive rates through their partner, Allianz. You will instantly acquire accident insurance.

About the role

In this part/section, we are going to look at the role that needs to be performed by the staff data engineer at LinkedIn.

Analyse

Organizations are increasingly often seen searching for the elusive, enigmatic  Data science Engineer in recent years. This position falls between a Data Analyst and a data engineer, as you may infer from the title.

Logic is the Data Scientist on the more sophisticated side of data analysis and business. They frequently create machine learning (ML) models and prepare data. Helping businesses gain better insights and make predictions based on data makes up a significant portion of their work.

These specialists use Python because it has excellent libraries for dealing with applications for data science.

Required Skill Set

In this part/section, we will look at the qualification required to be a staff data engineer at LinkedIn.

Skill Set

Basic Qualifications

🤹 Applied mathematicsstatisticscomputer science, operations research, engineering, economics, etc. Bachelor's degree in a quantitative field.

🤹 5+ years of experience working with significant amounts of data in a related industry or relevant academia

🤹 Knowledge of SQL and relational databases.

🤹 Knowledge of handling both organized and unstructured data at scale.

🤹 Knowledge of related technologies, including Hadoop and distributed data systems (Spark, Presto, PigHive, etc.).

🤹 Should be aware of at least one programming language.

🤹 It is a working knowledge of databases used to power front-end application APIs.

🤹 Knowledge of either front-end or back-end engineering, data processing, modeling, or both.

Preferred Qualifications

🚀 Computer sciencestatisticsapplied mathematics, etc., require a master's degree.

🚀 7+ years of experience working with significant amounts of data in a related industry or relevant academia

🚀 Knowledge in building Spark and Hive data pipelines.

🚀 Knowledge of data modeling.

🚀 Experience creating data-driven applications from the back or front end.

🚀 Profundity in relational and MPP databases' technological and functional designs.

🚀 Knowledge of data mining and reporting systems.

🚀 They know technologies like TableauR visualization packages, D3, and other Javascript libraries, as well as experience designing dashboards and displaying data.

🚀 Understanding of git, review boards, and systems similar to Unix.

Responsibilities of Staff Data Engineer at LinkedIn

Roles and responsibilities

In this part/section, we will look at the responsibilities that need to be performed by the staff data engineer at LinkedIn. Some of them are listed below:

📑 To find business opportunities and create scalable data solutions, collaborate with a group of high-performing analytics, data science, and cross-functional teams.

📑 Develop your data skills, assume business ownership, and manage intricate data systems for a single product or a range of related items.

📑 To support solutions that enable data-driven decision-making, do the essential data transformations.

📑 Design and architect databases and build and maintain data pipelines.

📑 Create effective systems or components for data flows or applications that do large-scale analysis, then execute, integrate, and document them.

📑 Make sure that teams in our data ecosystem share best practices and standards.

📑 It is essential to comprehend the analytical objectives to offer rational recommendations and encourage informed action.

📑 Work with internal platform teams to prototype and test solutions created in-house for extracting insight from huge datasets or automating intricate algorithms.

📑 Start working on tasks and see them through to completion with no direction.

📑 Engineering advancements that support LinkedIn's vision and objectives are welcome.

📑 Lead code/design reviews, mentor junior team members on technical issues, and provide technical direction.

Preparation Strategy

Preparation Strategy

In this part/section, we will learn how to prepare to select as a staff data engineer at LinkedIn. Check out the LinkedIn Preparation Guide for a detailed preparation guide to joining LinkedIn.

📘 Prepare for aptitude tests which most companies conduct even before the technical rounds.

📘 Then, it would help if you practiced DSA questions regularly. Once comfortable solving DSA questions, switch to practicing Competitive Programming questions. This will help you in solving real-life based questions in the online assessment.

Preparation Strategy

📘 For the interviews, prepare the core subjects like software engineering, DBMSoperating systemcomputer organization and architecture, and the language you have experience in, be it java or c++, or any other language.

📘 Also, prepare topics like CSS frameworks, the difference between HTML and CSSjavascript backend technologies, and application development using python. 

📘 Practice and prepare your projects very thoroughly. Projects can help you showcase all skills you have and how you have used them.

📘 There will generally be 3-4 technical interviews. The interviewer will cover questions from puzzles as well.

📘 Now, it comes to the HR round. Common HR interview questions would be asked in the round.

Resources

Resources

📌 Data Structures and Algorithms Web Technologies

📌 Aptitude 

📌 Competitive Programming 

📌 Java

📌 Python 

📌 Mobile Technologies 

📌 Guide to Open-Source 

📌 Ruby 

📌 IT Certifications 

📌 Database Management System

📌 Basic software testing and methodology

📌 Learn the basics of Python

📌 Resource to learn

📌 Computer Networks
 

You may look into the following to have better practical knowledge:

📌 Best Web Development Projects For Your Resume

📌 How to Practice for a Technical Interview? 

Career Path

So, you are determined to become a Data Science engineer. That is why you are here. You have already seen the perks and skills requirements in the above section.

But wondering how to gain excellence in this.

Well, Ninja! Don't panic. Coding Ninjas will help you through this.

To become a Data Science Engineer, you must have a strong background in math and computer science and experience working with vast amounts of data.

RoadMap for preparation

A strong background in mathematics and computer science

You will deal with a lot of data daily as a data scientist. You must therefore have a solid foundation in both computer science and mathematics. You should be exceptionally at ease using statistical techniques and algorithms.

Experience working with large amounts of data

Effective manipulation and analysis of massive data sets is a requirement for data scientists. As a result, before becoming a data scientist, you should have some prior experience working with massive data sets.

RoadMap preparartion

Experience with machine learning and statistical modeling

Data scientists use machine learning and statistical modeling as practical tools to conclude data. Anyone interested in becoming a data scientist must have experience with these methods.

A willingness to learn

Data scientists must be willing to constantly learn new methods and techniques because the field of data science is constantly evolving. Enrolling in a top data science education program is one of the best ways to learn how to become a data scientist or to sharpen your existing skills.

You can also check out Data Analyst vs Data Scientist here.

Frequently Asked Questions

What recruiters search in a data engineer?

Develop data models and pipelines for research, reporting, and machine learning in close collaboration with the data science and business intelligence teams. There are some common talents that many recruiters and hiring managers seek in data engineer candidates, even though every firm has unique needs.

What skills are required to be a data engineer?

An extensive range of technical abilities, including a solid understanding of SQL database design and several programming languages, are necessary for this IT position. But when they collaborate across departments and ascertain what corporate leaders hope to gain from datasets, data engineers should also possess excellent communication skills.

What does a data engineer do?

Data engineers are responsible for transforming unprocessed data into information that can be used for analytics and business decision-making. They deal with various systems from businesses, hospitals, and schools to constructing platforms. As a result, businesses can access their divisions' most accurate performance indicators.

What qualities must a data engineer possess?

A skilled data engineer must have great problem-solving abilities to apply data science to a company's business requirements. They must also be exceptionally technical in various areas, such as software engineering and programming languages, and have good communication skills because they will interact with company leaders daily.

Who works together with a data engineer?

Data scientists and engineers collaborate to increase the information's quality and accuracy, allowing organizations to make more responsible judgments. To support business choices, they collaborate with leaders from across the enterprise.

Conclusion

This article provides insight into what it takes to be a data engineer at LinkedIn. We had also seen the LinkedIn preparation strategy and career roadmap.

Want to learn more about the LinkedIn process, follow the link given below:

I hope you would have gained a better understanding of staff data engineers at LinkedIn now!

You can also consider our online coding courses such as the Data Science Course to give your career an edge over others.

Please upvote our blog to help other ninjas grow.

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