Big Data Engineer Job Description, Skills, and Salary
Get to know about the duties, responsibilities, qualifications, and skills requirements of a big data engineer. Feel free to use our big data engineer job description template to produce your own. We also provide you with information about the salary you can earn as a big data engineer.
Who is a Big Data Engineer?
Big data refers to massive amounts of customer, product, and operational data. These data are typically in the terabyte or petabyte ranges. Big data analytics can help optimize key business and operational use cases, reduce compliance and regulatory risk and generate new revenue streams. The volume and variety of data sets that make up big data are often the key factors. Volume refers to the total amount of data that can be derived from many sources, including social media, databases, and information from sensors or machines. There is also the rate of data received, which refers to the velocity. The variety examines the different types of data available. Big data is often unstructured and requires additional assistance. Big data engineers are here to help.
A Big Data Engineer is responsible for the development and design of data pipelines. They also collect data from different sources and organize it into sets that can be used by data scientists and analysts.
Big Data Engineers manage large numbers of complex data sets. As our world becomes more dependent on databases, the function of databases in managing and maintaining data systems and tools has become increasingly important.
They are responsible for data integration in central analysis infrastructure and determining the best technologies for this.
The big data engineer (IT professional) is also responsible for creating, testing, and maintaining large-scale data processing systems. This data specialist is responsible for aggregating, cleansing, transforming and enriching different types of data to allow downstream data consumers, such as data scientists and business analysts, to extract the information.
The demand for Big Data specialists is steadily increasing as more data is created every day. According to Forbes, Big Data Engineers are one of the top new jobs on LinkedIn. This position will require you to design and implement Big Data tools and frameworks. You’ll also need to collaborate with developers, build cloud platforms, and maintain the production system.
Big Data Engineer Job Description
Below are the big data engineer job description examples you can use to develop your resume or write a big 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 big data engineer include the following:
- Storing data in a data warehouse, or data lake repository.
- Using data processing transformations to transform raw data and create predefined data structures. For downstream processing, deposit the results in a data warehouse/data lake.
- Transforming and combining various data into a scalable repository of data (such as a cloud, data lake, or data warehouse).
- Learning about the different data transformation techniques, methods, and algorithms.
- Implementing technical processes and business logic to transform the data collected into valuable and meaningful information
- Understanding operational and management options as well as differences between data repository structures and massively parallel processing databases (MPP), and hybrid cloud.
- Comparing, evaluating, and improving data pipelines. This includes design pattern innovation and data lifecycle design.
- Setting up automated data pipelines to transform and feed data into production, QA, and dev environments.
- Collaborating with IT and data architects to achieve project objectives,
- Creating highly scalable data management systems from concept to completion
- Researching high-quality algorithms and predictive models
- Utilizing data set methods for data modeling, data mining, or data generation.
- Creating custom analytics software or other applications.
- Making sure that your data systems follow strict guidelines.
- Researching can improve data quality, reliability, and efficiency.
- Maintaining and improving manufacturing processes.
The following are other important tasks:
- Performance optimization
Performance is a key factor when dealing with big data platforms. Big data engineers must monitor the entire process and make necessary infrastructure changes to speed-up query execution. The following are some examples:
Database optimization techniques
Data partitioning is one of these techniques. It allows data to be broken down and stored in separate, self-contained parts. For a quick lookup, each data chunk is assigned a partition key. Database indexing is another technique that allows you to organize data in a way that speeds up data retrieval from large tables. To reduce the number of joins to tables, big data engineers use denormalization. This involves adding redundant data to one or several tables.
Efficient data ingestion
It becomes more difficult to transport data when it is constantly being accelerated in different formats. Big data engineers can use data mining techniques to find patterns in data sets and different data ingestion APIs to capture more data and inject it into the data lake.
- Stream processing
One of the most common jobs of big data engineers is to manage streaming flows.
Companies are leveraging transactional data and IoT devices to increase their efficiency. Data streams are unique because they have constant flow and are constantly updated, losing their relevance quickly. So, such data requires immediate processing. This is where a batch processing approach will not work. It is not possible to upload data streams to storage and then process them. Another approach is to concurrently process multiple streams. Event stream processors are big data engineers that feed data streams to them. They process the data simultaneously, keep it up-to-date, and then present it to the user.
- Implementing ML models
While it is not a core skill of a big-data engineer, it can be used if the data scientist isn’t proficient in creating production-ready code or building it in the pipeline. A big data engineer will need to create a corresponding model using ML in the data pipeline.
Qualifications
- Bachelor’s degree or master’s in computer engineering.
- Experience as a big-data engineer.
- Deep knowledge of Hadoop, Spark, and other similar frameworks.
- You should be familiar with scripting languages such as Java, C++, Linux, Ruby, PHP, and Python.
- Knowledge of NoSQL, RDBMS databases such as Redis and MongoDB.
- Familiarity with Mesos, AWS, Docker, and other tools.
Essential Skills
Big Data Engineers soft skills include:
- Communication skills
Big data engineers interact with data analysts, machine learning engineers, CTOs, developers, and data analysts every day. They may work with business units or other teams to collect requirements and determine the scope of a project. Effective collaboration requires communication skills. Big data engineers must also be able to communicate how their work will benefit the bottom line.
- Collaboration
Collaboration is essential when teams depend on one another for deliverables. They need to be able to establish a healthy relationship of giving and taking to ensure projects run smoothly. Big data engineers must understand their teams’ expectations, what they need, and their pain points. They can also help other teams understand the importance of this work and develop better collaboration ideas.
- Presentation skills
Big data engineers will need to present their findings and perform data analysis, depending on how large the team is. They can be persuasive orators by learning how to communicate technical data concepts in the context of solving business problems.
Big Data Engineers technical skills include:
- NoSQL and SQL database systems
SQL is the most common programming language used to build and manage relational databases (tables with rows and columns). NoSQL databases can be non-tabular. They come in many types, depending on the data model (e.g. graphs or documents). Bug data engineers need to be able to use database management systems (DBMS), a software program that allows for data storage and retrieval.
- Data warehouse solutions
Data warehouses can store large amounts of historical and current data that can be analyzed and questioned. These data are ported from many sources such as accounting software or CRM system. The organization then uses the data for data mining, reporting, analytics, and reporting. Employers expect entry-level engineers to be familiar with Amazon Web Services (AWS), which is a cloud service platform that offers a variety of data storage tools.
- ETL tools
ETL (Extract Transfer, Load), refers to the process of extracting data from a source and converting it into a format that can then be stored or analyzed in a data warehouse. Batch processing is used to assist users in analyzing data that is relevant to their business problems. The ETL pulls data out of different sources and applies rules to it according to business requirements. Finally, the ETL loads the transformed data into a database so that anyone can view and use it.
- Machine learning
Machine learning algorithms, also known as models, allow big data engineers to make predictions using historical and current data. Bug data engineers need to have a basic understanding of machine learning. This allows them to better understand the needs of data scientists (and thus, the needs and requirements for their organization), put models into production, and create more precise data pipelines.
- Data APIs
Software applications can be used to access data via an API. It allows two programs or machines to communicate for a specific task. APIs are used by web applications to enable the front-end user to interact with back-end data and functionality. An API is used to allow a website to make a request. It can read the database and retrieve the information it needs from the tables. The application then processes the request and returns an HTTP-based response. This is displayed in the browser. To enable business intelligence analysts and data scientists to query the data, big data engineers must be able to create APIs in databases.
- Programming Languages
Scala, Java, and Python are the most popular programming languages. Python is the most popular programming language for modeling and statistical analysis. Java is widely used in data architect frameworks, and many of their APIs were designed for Java. Scala, an extension to Java’s Java language, is interoperable with Java because it runs on JVM (a virtual computer that allows a computer to run Java programs).
Knowledge of these programming languages gives the big data engineer an edge.
How to Become a Big Data Engineer
- Earn a bachelor’s degree
A bachelor’s degree is the first step to becoming a big data engineer. Computer science is the most popular course for aspiring big data engineers. This position requires high-level computer skills as well as technical responsibilities. This will allow you to learn from the best in the field and study in-depth different concepts of computer science. Another option is to choose a related major such as information technology, software engineering, or systems administration.
- Get professional experience
Once you have completed your bachelor’s degree, you can apply for entry-level positions in the technology industry. It is a good idea to get some experience first so you can see what it’s like to work in technology. This will allow you to acquire key skills such as data analysis, programming, and system administration.
- Consider a master’s degree
Big data engineers are often able to choose to obtain a master’s degree, even though it is not required. An advanced degree will allow you to learn and practice more complex concepts than what you would encounter in an undergraduate program. This allows them to learn more about advanced methods of organizing, accessing, and storing data. A Master of Science degree in computer science is another common master’s program for these professionals.
- Get certified
Earning certification is another important part of being a big data engineer. Although some big data engineers may be able to find work without certifications, others will have better chances of getting a job. This is because specialty certificates can highlight your skills and show employers you are willing to put in the extra effort to improve them.
Where to Work
Big data engineers are needed in almost every industry. They provide insights that can be used for business, finance, and government as well as science and telecommunications.
Below is a list of companies where big data engineers can work:
- Internet of Things
IoT companies need fast data ingestion as they have many devices that are constantly sending data. Big data engineers will ensure that no critical information is lost in the data flow.
- Finance
Financial organizations need access to different kinds of data needed for processing. For this reason, big data engineers are needed in the industry.
- Social media companies
Social media companies make smart use of user data to understand their customers and how they interact with them so they can market their products. Social media can leverage cutting-edge technologies and even develop their own big data solutions.
- Marketing and eCommerce
Marketing and eCommerce companies can track every interaction users make with their websites online. This allows them to collect large amounts of data about customers.
This information is also spread across hundreds of web servers’ logs and many other systems. With this, big data engineers have a lot to do in this industry.
- Governmental and non-profit organizations
Big data is used by all levels of government, and in different forms. Data processing can be established by big data engineers where multiple datasets can be combined to provide the best insights.
Big Data Engineer Salary Scale
The average national salary for big data engineers in the United States is $116,781 per annum.
