What is a Data Engineer?
The Data Engineer job includes designing and building systems for storing, cleaning, and analyzing large quantities of data. Data engineer skills can be applied in almost any industry. To ensure that the data collected by organizations is usable when it reaches the data scientists and analysts, they need the right people and technologies.
Data that engineers deal with varies from organization to organization, in part because of sizing. With an increasingly large company, analytics architectures become more complex, which means engineers will be responsible for more data. Healthcare, retail, and financial services are some of the industries with more data-intensive processes. Combined with data scientists, data engineers enable businesses to make more accurate and trustworthy decisions through enhanced data processing and transparency.
What does a Data Engineer do?
In Data Engineer jobs, the goal is to collect and prepare data so that data scientists and analysts can analyze them. This is accomplished in three ways:
- It is most common to find generalists on small teams or in small companies. Among the few “data-focused” employees in the company, data engineers do a lot of different things. Generalists are often assigned to manage and analyze data at every stage of the data process. Smaller businesses will not need to worry as much about engineering for scale, so this role is for someone transitioning from data science to data engineering.
- Data engineers who work alongside data scientists are typically found in midsize companies to help them make use of the data they gather. Computer science and distributed systems are important skills for pipeline-centric data engineers.
- Those who focus on analytics databases in larger organizations are usually data engineers, as data flow management is full-time. A database-centric data engineer is responsible for creating schemas for tables across multiple databases as well as working with data warehouses.
Related Job Descriptions
Data Engineer’s job duties include:
- Design and maintain a data pipeline architecture that is optimal.
- Ensure that large, complex data sets satisfy functional and non-functional business requirements.
- Identify, design, and incorporate improvements to the internal process: improve data flow, streamline manual processes, re-design technology to ensure greater scalability, etc.
- Utilize SQL and AWS big data technologies to build the infrastructure necessary for effective extraction, transformation, and loading of data from different data sources.
- Build analytical tools that capitalize on data pipeline to provide actionable insights into customer acquisition, other key metrics of performance, and operational efficiency.
- Ensure data-related technical errors are resolved and corresponding data infrastructure needs are met by working with stakeholders, including the Product, Executive, Data, and Design teams.
- Develop analytical and data science tools for analytics team members so they can build and optimize our product to become an innovative industry leader.
- Ensure our data systems are as functional as possible by working with data and analytics experts.
Job brief for Data Engineer
A successful candidate for this Data engineer job should be able to analyze data from various sources as well as combine it. Knowledge of learning machine methods and familiarity with many programming languages are also essential skills for data engineers. Organizations seek candidates who are experienced in this field, detail-oriented and possess excellent organizational skills. Below are a few skills companies seek from applicants.
- Creating and maintaining an optimized data pipeline architecture
- Develop large, complex data sets to meet the demands of the business
- Identify and improve internal processes
- Redesign infrastructure for greater scalability and optimization of data delivery
- Implement SQL and AWS technologies to build the infrastructure necessary for extracting, loading, and transforming data from various data sources
- Assisting with data-related technical issues and supporting data infrastructure needs among internal and external stakeholders
- Make data tools available to analytics and data scientists.
Responsibilities of Data Engineer
A data engineer is responsible for organizing and managing data and keeping a close eye on trends or discrepancies that could affect business goals. Programming, mathematics, and computer science are among the skills and knowledge required. However, data engineers also require soft skills in communicating data trends to other company employees and guiding the firm in using the acquired data. A data engineer is often responsible for performing the following tasks:
- Building, testing, and maintaining architectures
- Ensure that architecture is aligned with business objectives
- Acquiring data
- Process the data sets
- Using programming languages and tools
- Determine how data quality, reliability, and efficiency can be improved
- Conduct market and industry research
- Adopt machine learning algorithms, sophisticated analytics programs, and statistical techniques
- The preparation of data to support predictive and prognostic modelling
- Analyze data to discover hidden patterns
- Automate tasks by analyzing data
- Provide stakeholders with analytics-based updates
Requirements for Data Engineer
- Working knowledge of SQL and experience developing queries using relational databases (SQL) and previous experience with a range of databases are required for data engineer jobs.
- Experience in developing and improving “big data” data pipelines, systems, and data sets is needed for data engineer jobs.
- An understanding of how root cause analyses are performed on both internal and external data and procedures to answer relevant business issues and determine opportunities to improve.
- An ability to analyze unstructured data sets.
- Manage workload and dependencies by building processes for data transformation, data structures, and metadata.
- Successfully manage, process, and derive value from huge disconnected datasets.
- Understanding of message queues, highly scalable ‘big data’ storage, and stream processing.
- The ability to manage projects and organize resources is needed for data engineer jobs.
Average Salary for Data Engineer Jobs
The average salary for data engineers in Malaysia can vary based on experience, location, and specific employer.
General Salary Overview
- Entry-Level (0-2 years of experience): Approximately RM 40,000 - RM 60,000 per annum.
- Mid-Level (3-5 years of experience): Approximately RM 60,000 - RM 90,000 per annum.
- Senior-Level (5+ years of experience): Approximately RM 90,000 - RM 150,000 per annum.
Salary by City
| City | Average Annual Salary |
|---|---|
| Kuala Lumpur | RM 80,000 |
| Petaling Jaya | RM 75,000 |
| Johor Bahru | RM 70,000 |
| Penang | RM 65,000 |
| Kota Kinabalu | RM 60,000 |
Salary by Industry
| Industry | Average Annual Salary |
|---|---|
| Information Technology | RM 85,000 |
| Financial Services | RM 80,000 |
| E-commerce | RM 75,000 |
| Telecommunications | RM 70,000 |
Salary by Top Employers
| Company | Average Annual Salary |
|---|---|
| Maybank | RM 120,000 |
| CIMB Bank | RM 110,000 |
| Telekom Malaysia | RM 100,000 |
| Accenture | RM 95,000 |
This overview indicates that data engineers in Malaysia can expect varying salaries based on their experience and the type of employer. The demand for skilled data engineers is increasing as organizations focus on leveraging data for decision-making.
Key Skills
- SQL Databases
- Programming Languages
- Hadoop Ecosystem
- Apache Spark
- NoSQL Databases
- Apache Airflow
- Amazon Redshift
- Apache Kafka
- ELK Stack
Why Pursue A Career As Data Engineer?
There are both rewards and challenges associated with a career in this field. Data scientists, analysts, and decision-makers will be able to easily access the data they need to conduct their jobs thanks to the data you provide. Scalable solutions require your programming skills and problem-solving abilities. Consequently, there are opportunities for pay scales since this generation is digitally savvy.
Related Article:
How to Become a Data Engineer?
Computer scientists, engineers, applied mathematicians, and other IT professionals typically study data engineering. Data engineers are required to possess a high level of technical skills, so a certification or boot camp alone may not be enough. According to PayScale, most data engineer jobs require a bachelor’s degree in a relevant field.
Python and Java are among the programming languages you’ll need to master, as is an understanding of SQL databases. An IT boot camp or certification can help you tailor your resume to data engineer jobs if you have a background in IT or a related field, such as mathematics or analytics. You might enroll in a data science boot camp if, for example, you have worked in IT, but not in a related data engineer job, to prove that you have the skills along with your IT experience.
FAQ on What is a Data Engineer
Q1. What is a Data Engineer?
A1. A Data Engineer is a professional responsible for designing, building, and maintaining the infrastructure and systems required for collecting, storing, and analyzing large amounts of data. Data engineers work closely with data scientists, analysts, and other stakeholders to ensure that data is accessible, clean, and reliable for analysis.
Q2. What skills are required to be a Data Engineer?
A2. To be a successful Data Engineer, you should have skills in the following areas:
- Programming (Python, Java, SQL)
- Data modeling & ETL processes
- Big Data tools (Hadoop, Spark, Kafka)
- Cloud platforms (AWS, GCP, Azure)
- Data warehousing (Redshift, BigQuery, Snowflake)
Q3. What does a Data Engineer do on a daily basis?
A3. On a daily basis, a Data Engineer may engage in the following tasks:
- Build and optimize data pipelines
- Data cleaning and transformation
- Collaborate with data scientists and analysts
- Monitor and troubleshoot data systems
Q4. How does a Data Engineer differ from a Data Scientist?
A4. The key difference between a Data Engineer and a Data Scientist is the focus of their work:
- Data Engineer: Focuses on the architecture, infrastructure, and systems needed to collect, process, store, and transport data. They build the foundation for data analysis.
- Data Scientist: Focuses on analyzing and modeling data to generate insights, predictions, and business value. Data scientists typically use the data pipelines and systems set up by data engineers to perform advanced analysis.
In short, Data Engineers build the systems, while Data Scientists work with the data within those systems.
Q5. What are the common tools and technologies used by Data Engineers?
A5. Data Engineers commonly use a wide range of tools, including:
- Databases: MySQL, PostgreSQL, MongoDB
- Big Data: Hadoop, Spark
- Cloud: AWS, GCP, Azure
- ETL: Apache Airflow, Talend
- Data Warehousing: Snowflake, Redshift
