Data engineer and the path to becoming a data engineer (DE) with 4 steps

Tram Ho

Data Engineer, also known as data engineer, is one of the important positions in the field of data science. With the development of the digital era, the need for digital transformation of businesses is increasing, making the Data Engineer position an important role in the success and development of enterprises.

What is Data Engineer?

Data Engineer or data engineer plays the role of building systems, synthesizing, storing and exporting data in enterprises. With the complex and specific nature of the job, a Data Engineer needs to have in-depth knowledge and skills in using tools to serve the job.

Data engineers need to use their knowledge and skills to find trends in enterprise data, find solutions and take charge of improving the quality of data sources.

Daily work of a Data Engineer

Main job of a data engineer

With Data Engineer, their task will be to build a data infrastructure system to analyze, operate and integrate systems together. A data engineer will do work related to the data system operating structure, design data pipelines to serve the development needs of the Data Warehouse, be responsible for the operating system. enterprise data state.

Data Engineer is also in charge of processing, formatting and optimizing the data pouring into the Data Warehouse to suit each use purpose. Simply put, a data engineer will have to make sure that all the data that comes in is always ready to serve the work of other positions such as Data Analyst and Data Scientist.

The specific day-to-day work of a data engineer

Data engineers often take on tasks similar to the following:

  • Data Infrastructure Architectural Design: At its core, data engineering entails designing the architecture of a data platform.
  • Development of data related tools/versions: As data engineers, from the outset, these professionals will use programming skills to develop, customize, and manage integration tools. integrations, databases, data warehouses, and analytical systems.
  • Data pipeline maintenance/testing: During the development phase, data engineers will test the reliability and performance of each part of the system. Or they can partner with the testing team.
  • Data and Metadata management: Data can be stored in a warehouse in a structured or unstructured manner. Additional storage may contain metadata (discovery data about data). A data engineer is responsible for managing stored data and structuring it logically through a database management system.
  • Provide data access tools: In some cases, such tools are not required, as Data Scientists can use data warehouse types such as data-lake to pull data right from the warehouse storage. However, if an organization requires business information for Data Analysts and other non-technical users, data engineers are responsible for setting up the tools to view data, generate reports, and create images.
  • Monitor Data Pipeline Stability: Monitor overall system performance and stability to ensure that Data Warehouse needs to be cleaned up from time to time. Data Pipeline automations also need to be monitored and modified as data/models/requirements can change.

The role of Data Engineer position for businesses

Depending on the nature of the business, the Data Engineer can be divided into many different departments with different roles

  • Generalist – Generalist Generalist generalist job is to collect, enter, and process data. They will have more skills than most data engineers but will not specialize in any field of work and have little knowledge of system architecture.
  • Pipeline-centric – In charge of data pipelines Many businesses with complex data analysis needs often require Data Engineers to focus on data pipelines. They will be in charge of building the data flows, ensuring the data flows in order to convert the data into a format useful for analysis.
  • Database-centric – In charge of the database segment Database-centric has the main task of deploying, maintaining and putting into the analytical database. These data engineers often exist in large companies where data is spread across multiple databases.

Engineers use pipelines, tune databases for efficient analysis, and create table schemas using extract, transform, load (ETL) methods. ETL is the process of copying data from multiple sources into a single target system.

Why should you choose Data Engineer career?

Data Engineer is a rather complex and highly specific job. To become a Data Engineer requires us to have a strong grasp of the knowledge and skills of programming languages, algorithms, and complex tools.

Moreover, with the non-stop development of 4.0 technology, the need for enterprise digital transformation is huge and Data Engineer is considered to hold a key position. There will be no more piles of papers and huge documents, which will all be digitized, saving time and reducing costs for businesses.

Data engineer will be the person who builds up the entire structure, infrastructure system of the enterprise, is likened to the head agency for development and operation of related positions. image.png According to statistics, Data Engineer is ranked in the Top of the highest paying jobs in the world. In Vietnam, the average salary of a Data Engineer is up to 30 million/month and fluctuates depending on the ability and experience of each person.

The development of technology 4.0 has created a career trend in the present and in the future. It is forecasted that the recruitment demand of Data Engineers of enterprises is very large and the income level of a Data Engineer will increase in the future.

Skills required to become a Data Engineer

Basic skills a data engineer must have

  • Data Modeling (data modeling), Data Warehouse (data warehouse), Data APIs (Restful API for data) and Data Lake.
  • Coding: Proficiency in programming languages ​​is essential for this role. Popular programming languages ​​include SQL, NoSQL, Python, Java, R, and Scala. (Mostly SQL and Python, if Scala any better)
  • Spark to build a data system. At least everyone should understand how Spark works; and write Spark Application.

These are the skills required in a Data Engineer to be able to work; In addition, you should also hone other professional skills to better serve the job.

Complementary skills for the job of a data engineer

  • Devops: Docker, Kubernetes – used to deploy services, data applications
  • Machine learning: Although Machine learning is the primary field of data scientists; but it can be helpful if you grasp the basic concepts for better understanding; needs of the data scientists on your team that know how to effectively support.
  • Big Data tools: Data engineers don’t just work with regular data. They are often tasked with managing big data. Tools and technologies are evolving and changing from company to company; but some popular tools include Hadoop, MongoDB and Kafka.
  • Cloud computing. You will need to understand cloud storage and cloud computing as companies increasingly run on servers for cloud services; popular are Amazon Web Services (AWS) or Google Cloud.
  • Data security: Although some companies may have dedicated data security teams; but many data engineers are still tasked with managing and storing data; a secure way to protect data from being lost or stolen.

To become a Data Engineer, what do you need to learn?

Due to its specificity, to become a Data Engineer, you need to understand many different knowledge and skills to serve the job.

Programming language

  • SQL: Data engineers will often have to work with SQL databases to set up, query, and manage database systems.
  • Python: Data engineers use Python to code ETL frameworks, API interactions, automation, and data aggregation tasks like reshaping, aggregating, combining disparate sources, etc.
  • R: Used to perform statistical analysis and data visualization. R’s statistical functions also make it easy to clean, enter, and analyze data.
  • Scala: When it comes to Data Engineer, Spark is one of the most widely used tools and it is written in Scala language. Scala is an extension of the Java language. If you are working on a Spark project then Scala is the language you should learn

Relational and Non-Relational Databases

Data engineers must also know how to work with relational database systems. Such as MySQL and PostgreSQL. Besides, Data Engineers should also have skills in working with NoSQL non-relational databases; like MongoDB, Apache Cassandra, Couchbase and Apache HBase

ETL/ELT . Engineering

Data Engineers also need to know how to use ETL tools; to move data from databases and other sources into a single repository; such as Data Warehouse. Popular ETL tools include Xplenty, Stitch, Alooma, and Talend

Data Warehouse/Data Lake

Data in organizations and businesses from sources such as CRM systems; accounting software and ERP software extracted by Data Engineers; processing and storing in a data storage system; it could be a Data warehouse or a Data lake; then these data are collected by Data Analysts, Business Analysts or Data Scientists,…; used for reporting, analysis and data mining.

  • Data Lake is a data warehouse that stores all kinds of data; whether structured, unstructured or semi-structured. It contains a large amount of data in its original format. Usually only large companies and corporations with a lot of data need to build a Data Lake.
  • Data Warehouse is the company’s data warehouse; usually store only modeled/structured data

Build analytical reports

Business Intelligence (BI) data visualization and analysis tools; and the ability to configure them is also important knowledge that data engineers need. With the BI platform, Data Engineers can establish connections between Data warehouses; Data lakes and other data sources. Data Engineers must know how to visualize data on Power BI, Python or R; as well as make general reports.

Machine Learning

Machine learning algorithms — also known as models — help Data Scientists; Make predictions based on data. Data Engineers only need basic knowledge of Machine learning; because it allows them to better understand the needs of the Data Scientist as well as the needs of the organization; from there can put the model into building Data pipeline (data pipeline) more accurately.

How is Data Engineer different from Data Analyst?

Both positions work with data and almost overlapping knowledge. However, there is a clear difference between these two terms

With Data Analyst, their main task is to participate in the process of information extraction, data analysis and final results. As for Data Engineer, their main job will focus mainly on designing and building data infrastructure structure.

They will be the ones to build the data warehouse (Data Warehouse) and data streams (Data Pipeline) and ensure that the data is circulated and always in a ready state, serving the work of the Data Analyst.

Due to the nature of being responsible for the entire data ecosystem of the enterprise, to become a Data Engineer, you must hold in-depth, advanced knowledge. Data Engineers must work with both structured and unstructured data.

Therefore, they need to have expert knowledge of both SQL and NoSQL databases. Data Engineer also needs to have more experience with advanced programming language knowledge and skills such as Python, Java, Scalar, etc.

Because of the high demand from advanced knowledge and skills, directly building a database structure to serve other sets, Data Engineers will have a higher income than Data Analysts in the field of data science. Whether

If you are passionate about becoming a data engineer, this will be most suitable for those of you who already have an IT background, deep understanding of complex programming languages. These knowledge are necessary to be able to perform advanced operations such as Data Lake and Big Data.

For those who are working as Data Analysts who want to switch careers to Data Engineers, it also has an advantage because the knowledge to learn seems to be quite overlapping, but you will still have more difficulties than those with IT knowledge because you have to be sure. In-depth and complex knowledge of programming languages.

According to INDA’s survey on more than 100 students participating in data engineer training here, nearly 60% of students are working or have a background in IT, programming. image.png

Training path to become Data Engineer at INDA

The Data Engineer course at INDA provides a structured, professional pathway for anyone who aspires to become a data engineer. The knowledge at INDA is trained from basic to advanced knowledge by experienced instructors and teaching assistants Website:

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Source : Viblo