Data Engineer Job Description, Skills, and Salary
Get to know about the duties, responsibilities, qualifications, and skills requirements of a data engineer. Feel free to use our data engineer job description template to produce your own. We also provide you with information about the salary you can earn as a data engineer.
Who is a Data Engineer?
Data engineers are IT workers whose main job is to prepare data for operational or analytical purposes. This group of software engineers is responsible for creating data pipelines that bring together information from various sources. They combine, consolidate, cleanse and organize data for use in analytics software. They strive to make data accessible and optimize their company’s big-data ecosystem.
The more complex an organization’s analytics architecture is, the more data engineers will have to manage. Some industries, such as healthcare, financial services, and retail, use more intensive data than others.
Data engineers are part of data science teams. They improve data transparency and enable businesses to make better business decisions.
They are also responsible for preparing and collecting data to be used by analysts and data scientists.
These three roles are the most important:
- Generalists
Generalist data engineers work in small teams and are responsible for all aspects of data collection, intake, processing, and analysis. Although they may be more skilled than other data engineers, they have less knowledge about systems architecture. The generalist role would suit a data scientist who wants to become a data engineer.
- Pipeline-centric engineers
These data engineers work in a middle-sized data analytics team, and on more complex data science projects across distributed systems. This role is more common in large and mid-sized companies.
One regional delivery company might embark on a pipeline-centric project to develop a tool that allows data scientists and analysts to access metadata to find information about deliveries. They might analyze the distance driven and delivery time for deliveries in the last month. Then, they could use this data in a predictive algorithm that will show what the company’s future business opportunities are.
- Database-centric engineers
Data engineers are responsible for implementing, maintaining, and populating analytics databases. This is a common role in larger companies, where data may be spread across multiple databases. Engineers work with pipelines to optimize databases for efficient analysis. They also create table schemas by using extract, transform and load (ETL). ETL refers to the process of transferring data from multiple sources into one destination system.
An analytics database would be a database-centric project for a multistate, national or large food delivery service. The data engineer would create the database and write the code to transfer data from the main database to the analytics database.
Many data engineers work alongside data scientists as part of an analytical team. Data scientists run queries against the data and create algorithms to extract the relevant data from the engineers. Data engineers also provide aggregated data to business executives, analysts, and other end-users so that they can analyze and use the results to improve business operations.
Data engineers work with structured and unstructured data.
Structured data refers to information that can be organized in a structured repository such as a database.
Unstructured data, such as text, images, and audio files, don’t follow the same data model. Data engineers need to be able to understand the differences in data architecture and how to use them to manage both types of data. The data engineer’s toolkit also includes a variety of big-data technologies such as open-source data extraction and processing frameworks.
Data Engineer Job Description
Below are the data engineer job description examples you can use to develop your resume or write a data engineer job description for your employee. Employers can also use it to sieve out job seekers when choosing candidates for interviews.
The duties and responsibilities of a data engineer include the following:
- Getting data that is compatible with your business needs
- Creating algorithms to convert data into actionable, useful information
- Constructing and generating infrastructure to allow big data to access and be analyzed.
- Preparing raw data to be used by data scientists.
- Correcting errors in your work.
- Assuring that your work is always backed up and easily accessible to all relevant coworkers.
- Staying current with technological advances and industry standards that will improve your outputs is key.
- Designing, constructing, testing, and maintaining the architecture
- Aligning architecture and business needs
- Developing data set processes
- Finding ways to increase data reliability, efficiency, and quality
- Researching for business and industry questions
- Using large data sets to solve business problems
- Embracing advanced analytics programs, machine learning, and statistical methods
- Preparing data for predictive and predictive modeling
- Finding hidden patterns using data
- Providing updates to stakeholders based upon analytics
The following are other important tasks:
- Extracting data
Data is located in a particular source, so we need to first extract it. For corporate data, the source could be a database, website user interactions, or an internal ERP/CRM. A sensor mounted on an aircraft’s body could be the source. The data could also be obtained from online public sources.
- Data storage/transition
Storages are the main architectural feature of any data pipeline. It is necessary to store the data that has been extracted. A data warehouse is a place where all data collected for analysis purposes can be stored.
- Collecting Data
Before they can start any work on the database, data engineers must first obtain the correct data. Data engineers then store optimized data after creating a set of data processing.
- Enhancing Skills
Data engineers are not limited to theoretical concepts. They need to be able to work in any programming environment, regardless of the language they use. They must also keep up to date with machine learning algorithms such as the decision tree, random forest, k-means, and others.
They are skilled in using analytics tools such as Apache Spark, Knime, Tableau, and Knime. These tools are used to provide valuable business insight for many industries. Data engineers, for example, can help improve the diagnosis and treatment of patients by identifying patterns in patient behavior.
- Track pipeline stability
As long as the warehouse is cleaned, monitoring the system’s overall performance and stability is important. The automated parts of a pipeline should also be monitored and modified since data/models/requirements can change.
Qualifications
- Bachelor’s degree is required in data engineering, big-data analytics, computer engineering, or a related field.
- It is beneficial to have a master’s degree in the relevant field.
- Demonstrable experience as a data engineer or software developer.
- Expert proficiency in Python and C++, Java, R, SQL, and Java
- Familiarity with the usage of Hadoop
- You have excellent problem-solving and analytical skills.
- You have a natural ability to work in groups and be independent.
- A meticulous approach to duties.
- Ability to manage a large number of tasks with little supervision.
Essential Skills
- SQL
Data engineers are responsible for moving a lot of data every day. There are two main types of database technologies: SQL and NoSQL.
Strong SQL skills enable you to use databases to build data warehouses, integrate them with other tools and analyze that data for business purposes. Data engineers may choose to focus on one of several SQL types (Advanced Modelling or Big Data). However, it is important to understand the basics of this technology before you can achieve your goals.
All companies, big and small, require data engineers who are proficient in SQL.
- NoSQL
This is a new type of distributed storage, which is becoming more popular. The name “NoSQL”, as it is commonly understood, refers to technology that uses a different type of SQL.
Apache River, BaseX, and Ignite are all examples of NoSQL. These terms will be used in your job search as a data engineer, so it would be an advantage to know how to use them.
- Python
Python, the most popular programming language, is still in high demand. It’s the third most beloved by programmers. To be able to write complex, maintainable, and reusable functions, data engineers must be proficient in Python. This language is versatile, efficient, great for text analytics, and provides a solid foundation for big-data support.
- Amazon Web Services (AWS).
AWS is a well-known cloud platform that programmers use to be more innovative, agile, and scalable. AWS is used by data engineering teams to create automated data flows. This tool will help you to understand the design and deployment of cloud-based data infrastructure.
You might consider taking online courses to learn AWS or Amazon’s tutorials on the subject (such as this one on AWS/big data). You can then test your knowledge and receive an Amazon official certificate – a great way to be recognized as a professional.
- Kafka
Kafka is an open-source platform for processing real-time data streams. This means that you can use it for real-time streaming apps. This is what businesses need. Kafka-powered apps can detect and apply trends, and respond almost immediately to customer needs.
This is why 60% of Fortune 100 companies use Kafka to build their applications. Target, Microsoft, Netflix, and Airbnb are just a few of those that use Kafka. The New York Times uses Kafka to store published content and make it available to its readers.
- Hadoop
Apache Hadoop, an open-source framework for data engineers, allows them to store and analyze large amounts of information. Hadoop isn’t just a platform, but rather a collection of tools that allow data integration. This is why Hadoop is useful for big data analytics.
Kafka and Hadoop will be used together if you are a data engineer.
- Clarity and concise writing
Writing is the first soft skill on this list. Many data engineers are unable to write well, which can lead to them missing out on better job opportunities. These are the top benefits of writing for data scientists:
Solidify your writing knowledge by writing blogs to strengthen your understanding.
Reporting data and results to managers, colleagues, or other parties may be your responsibility. This requires you to communicate clearly and concisely.
Grammarly is a free tool that will help you check your writing. It will identify complex sentences and unneeded words and make recommendations to improve writing clarity and coherence.
- Communication skills
Data engineers are people who communicate with many stakeholders including chief technology officers, data analysts, data designers, clients, data scientists, developers, and other users.
LinkedIn research revealed that communication, including interpersonal communication, is the most sought-after soft skill by employers. You need to learn interpersonal communication skills regardless of whether you are an introvert or not.
How to Become a Data Engineer
- Learn the correct programming languages
If you want to become a data engineer, you can start by improving your programming skills and learning the programming languages used data by engineers. Data engineers use SQL to create and manage relational databases. Next, you will learn how to use these languages in real-world situations.
- Learn automation and scripting
Automating many of the tasks involved in transforming and analyzing data is possible, particularly if it is repetitive or takes too long. You will need to be familiar with the syntax and operations of scripting languages, as well as product configurations, such as workflow processes, escalations, and actions, to automate tasks. You can use scripting languages to automate tasks or extract information from a database.
- Find out how databases work
Data engineers deal with both structured and unstructured databases. Relational databases are tables that contain rows and columns of structured information. SQL is used by data engineers to transform and transport data using ETL pipelines from a data source, such as a relational database, to a warehouse. They can also tune databases to perform fast analysis and create table schemas. Unstructured data can be stored in NoSQL databases as documents. A proprietary language is required to query a NoSQL database. It is quite different from SQL.
- Find out how data processing works
Data processing refers to the conversion of raw data into the analyzable format. Apache Spark is the most popular engine for parallel data processing, which is great for large datasets. This data processing framework uses batch processing. It involves collecting data points and grouping them within a specific period. Stream processing is continuous data collection in real-time. Each model has its use case. Batch processing is best when you don’t need real-time data. Stream processing is crucial for maintaining business intelligence current.
- Get the knowledge of cloud computing
Cloud platforms have the advantage of centralizing processing power, which allows companies to store unlimited amounts of data without incurring additional storage costs. Amazon Web Services and Microsoft Azure are the most popular cloud platforms. Certain job descriptions will require knowledge of a particular platform. Data engineers can use cloud platforms to access a variety of services, such as MPP databases that run on multiple machines.
- Create a portfolio
Consider the problems that data science can solve and what data sources and pipelines are needed to query them. Data science can solve real-world problems such as predicting oil demand and pricing, election outcomes, iceberg paths, reproduction rates, and other issues. Make a problem statement in a discipline that is important to you. Next, identify the datasets that you will need and determine if they are publicly available. Collect the data, and create a pipeline to store and query it.
Where to Work
The top industries that employ data engineers are those that have a large number of data-driven sets. This covers IT, healthcare, financial services, and computer software firms.
Data Engineer Salary Scale
The average salary of a Data Engineer in the United States is $93,147.
