Remote data engineer jobs at top U.S. companies

The core focus of data engineering is to build dependable infrastructures that enable constant data flow in a data-driven environment. Data engineers function as facilitators of clean and raw data from multiple sources so that businesses can use it to make data-driven choices.

Data engineering is the process of developing and constructing large-scale data collection, storage, and analysis systems. Data engineers design systems that gather, handle and turn raw data into usable information for data scientists and business analysts to comprehend. The ultimate objective is to make data more available so that businesses may assess and improve their performance.

The main responsibility is to conceptualize and build a dependable infrastructure for translating data into meaningful insights that data scientists can understand. Remote data engineers must be able to identify trends in massive datasets in addition to designing scalable processes to transform semi-structured and unstructured data into usable representations. Let's take a look at some of the other responsibilities of remote data engineer jobs.

• Develop, construct, test, and maintain data architectures
• Assemble complicated datasets that align with business requirements
• Deploy sophisticated analytics programs, machine learning, and statistical methods
• Ensure data security and governance with modern-day security controls
• Translate complex functional and technical needs into elaborate designs
• Implement data storage with technologies like Hadoop, NoSQL, etc.
• Integrate data management processes into the organization’s current structure
• Help in seamless third-party integration
• Create high-performance and scalable web services to track data

Data engineers usually have a background in Computer Science and Engineering, Applied Mathematics, or a related IT profession. However, coming from a non-tech background, it is still possible to become a Data Engineer.

The Data Engineer job necessitates a high technical understanding of data structuring and storage. If you're still pursuing studies, you can opt for technical degrees like Computer Science, Data Engineering, or Machine Learning. A bachelor's degree in Computer Science or a similar subject is common among data engineers. By acquiring a degree, you can establish the foundation of knowledge in this rapidly changing sector. You can also pursue a master's degree to enhance your career and gain access to possibly higher-paying opportunities.

You'll require programming skills in a variety of languages, including Python and Java, as well as an understanding of SQL database architecture. A boot camp or certification can help design a CV for remote data engineering jobs if you already have a background in IT or a related area like mathematics or analytics.

If you don't have a background in technology or IT, you can choose the self-learning or mentorship programs. Mentorship programs are online courses available on different professional education platforms that provide guided learning courses (one on one). Self-learning is the path many choose because of the vast number of technical resources available on the Internet today, but this is unguided learning, so it may take more time/resources. Keep checking details about master's degrees in data analytics and data engineering if you have an undergraduate degree, but it isn't in a relevant discipline.

Take some time to look through job postings to discover what employers are looking for, and you'll better understand how your experience fits into that role.

1. Hadoop and Spark

The Apache Hadoop software library is a framework that uses basic programming principles to enable the distributed processing of massive data volumes across clusters of machines. It's built to expand from a single server to thousands of devices, each with its computing and storage capabilities.

Python, Scala, Java, and R are among the programming languages supported. While Hadoop is the most powerful tool for large data, it has several limitations, including slow processing and a high level of coding.

Apache Spark is a data processing engine that allows stream processing, which involves continuous data input and output. It is similar to Hadoop in that it performs many of the same activities.

2. C++

C++ is a very basic yet powerful programming language for swiftly calculating massive datasets. It's the only language that can handle over 1GB of data in a single second. The data can be retrained and predictive analytics can be used in real-time while keeping the system of record consistent.

3. Data warehousing

A data warehouse is a relational database to query and analyze data. It's intended to provide a long-term picture of data across time. A database, on the other hand, continuously updates real-time data. Knowledge of systems like Amazon Web Services and Amazon Redshift is needed for data engineering. In fact, AWS is a prerequisite in various on-site and remote data engineer jobs.

4. Azure

Azure is a cloud platform from Microsoft that allows data engineers to create large-scale data analytics solutions. With an easy-to-deploy bundled analytics solution, it simplifies the deployment and support of servers and applications.

The package includes pre-built services for everything, from data storage to powerful machine learning. Azure is so popular that some data engineers even specialize in it.

5. SQL and NoSQL

Database management systems (DBMS) is a software application that offers an interface to databases for information storage and retrieval - required knowledge for data engineers.

The SQL programming language is the industry standard for creating and maintaining relational database systems. On the other hand, non-tabular NoSQL databases come in several shapes and sizes depending on their data models, such as a graph or a text.

6. Machine learning

Data scientists use machine learning algorithms to create various predictive models based on current and past data. Data engineers, however, only require rudimentary knowledge of machine learning to better understand the needs of data scientists (and by extension, the needs of the company), and construct more accurate data pipelines.

7. Data APIs

An API is a data access interface for software applications. It enables two apps or devices to interact with one another to complete a certain job. Web applications, for example, employ API to interact between the user-facing front-end and the back-end functionality and data.

An API allows an application to read a database, get information from relevant tables in the database, process the request, and deliver an HTTP-based response to the web template, which is then shown in the web browser. Data engineers provide APIs in databases for data scientists and business intelligence analysts to query the data.

8. Extract, Transfer, Load (ETL)

ETL (Extract, Transfer, Load) is the process of extracting data from a source, converting it into a format that can be analyzed, and storing it in a data warehouse.

The ETL collects data from a variety of sources, applies business rules to the data, and then loads the transformed data into a database or business intelligence platform where it can be accessed and utilized by everyone in the company.

Becoming a data engineer is highly rewarding. However, you need to have a thorough understanding of programming. Practicing programming is important, along with having a vision of the product. Good communication skills are also helpful in collaborating with team members and prioritizing work.

To simplify your search for data engineer remote jobs, Turing has made things a little easier. We offer the best opportunities to suit your career trajectory. Join a network of the world's top developers and get full-time, long-term remote data engineer jobs with better compensation and career growth prospects.

Turing helps you suggest a salary range that allows you to settle for a fruitful and long-term opportunity. Most of our recommendations are an assessment of market conditions and the demand set by our clients. However, at Turing, we believe in flexibility. Thus, every Data engineer is eligible to fix their salary range as per their skills and expertise.