Data Engineering: An exciting career path in Analytics and Information Technology
In today’s data-driven business landscape, the role of Data Engineering is all the more essential. Businesses are driven to quantify their consumers’ behavior patterns through data. Leading to a steep demand for Data Engineers across developing and developed economies alike.
Data Engineering comes with the promise of readily-available open positions and high salaries. But is Data Engineering the next step in your career? Read this blog to know all about this fast-growing and dynamic engineering stream.
What is Data Engineering?
Data Engineering is a subset of computer engineering that deals with large-scale data processing.
Who is a Data Engineer?
Data Engineers are responsible for finding, evaluating, and implementing appropriate data storage solutions to store the voluminous amounts of data. They are also in charge of moving data from one storage solution to another that meets the organization’s requirements.
What does a Data Engineer do?
Data Engineers are now considered the backbone of the IT industry. They do various tasks and typically require a strong background in mathematics and programming.
A Data Engineer’s job is to take raw data and turn it into something that can be used by either an organization’s business intelligence or analytics department.
Ok. Then who is a Big Data Engineer?
A Big Data Engineer is still a Data Engineer, but they handle large volumes of data that come in a very high velocity and variety. So at what threshold does data become big data? Read these pointers below.
- Volume: When working with big data, you typically process high volumes of low-density, unstructured data. Twitter data feeds are a great example. Big data volumes could range from terabytes to petabytes of data.
- Velocity: Velocity is the rate at which data is collected/ingested and reacted to. A few IoT products operate in real-time and require real-time responses.
- Variety: In big data, variety refers to the several types of data that a data engineer will encounter. They are classified into structured data, unstructured data, and semi-structured data. Traditional data types were structured and fit neatly in a relational database. But with the rise of big data, data comes in new unstructured data types. Good examples of unstructured and semi-structured data types include text, audio, and video — these types of data naturally require additional preprocessing to analyze.
Let’s see the difference between Data Analyst vs. Data Engineer vs. Data Scientist
Data Analyst: Data Analyst analyzes numeric data and use it to help companies make better decisions.
Data Engineer: A Data Engineer is involved in cleansing and preparing the data for analysis. So Data Engineers develop, construct, test, and maintain the complete architecture.
Data Scientist: A Data Scientist analyzes and interprets complex data. They are data wranglers who organize (big) data.
Data Engineering is the process that precedes Data Analytics.
How to Become a Data Engineer
Data Engineers are in high demand, with more and more companies looking for people who can handle big data. In addition, Data Engineering is evolving fast with newer technologies emerging in the market.
Data Engineering is an emerging field, so there are no set rules or guidelines for career paths. But there are some steps to get started. Knowledge in Computer Science, Math, and Statistics is essential. Additionally, you should also explore the various tech stacks and tools that Data Engineers use.
Here are a few of the more popular Tech Stacks and Tools Data Engineers use. And this is by no means a complete list.
- Amazon Athena
- Amazon Redshift
- Apache Spark
- Apache Hadoop
- Apache Airflow
As a Data Engineer, what could you expect your job to be like?
A Data Engineer is primarily responsible for data engineering tasks in a project.
There are three main role buckets for Data Engineers. They are:
- A Generalist: Generalist Data Engineers are typically found on small project teams. Here, Data Engineers play a versatile role. They are responsible for every step of the data process, from managing to analyzing it. This is a good starting point to transition from data engineering to data science.
- Data Pipeline Specialist: Data Engineers specializing in data pipelines are often found in midsize companies. They work with Data Scientists to leverage the data they collect.
- Database-centric: In larger projects, you will be responsible for the flow of data. Therefore focusing on data warehouses, databases, and developing table schemas.
The role of a data engineer is diverse. They are primarily responsible for extracting insights from raw data and handling the engineering side of things (hadoop clusters, SQL databases, python scripts). They are also tasked with architecting and building these systems and managing the operational side of things (logging errors, monitoring clusters, etc.). Between the three roles mentioned above, you will be tasked with some or all of these responsibilities.
- Develop, construct, test, and maintain architectures as per business requirements
- Data acquisition and developing processes
- Improve data reliability and quality
- Use data sets to address business problems
- Prep data for predictive and prescriptive modeling to find hidden patterns
- Automate tasks whenever possible with the help of data
A day in the life of a Data Engineer could be action-filled. Data Engineers spend a lot of time working through problems from various angles. And this makes their life very exciting and interesting.
Right Qualities to become a successful Data Engineer
Data Engineering is a highly technical role, requiring experience and skills in Programming, Math, and Computer Science. But data engineers also need communication skills to convey data trends to business counterparts.
Here are some of the qualities that could make you successful in a Data Engineering role.
Being a Self-Starter: The best engineers are self-motivated and always up for a challenge.
Creative Problem Solver: Data Engineers need to find innovative solutions to problems that others might overlook. You’ll need strong analytical skills to tackle issues when they arise, but don’t forget your creative side too.
Highly Organized: Data engineers need to be detail-oriented to handle all the various aspects of their jobs — from managing schemas and databases, tracking changes, following up on bugs, etc.
Data is undoubtedly both the current and the future. One of the best ways to prepare for a future built around data is becoming a Data Engineer. This career path will allow you to use your math, coding, and design skills to make sense of the world to businesses of all sizes.
If you are interested in Data Engineering as a career path, we suggest exploring openings at Ideas2IT. While we do hire Data Engineers, we also provide specialized guidance and training to engineers from other technologies so that they could to migrate to Data Engineering.
Ideas2IT was founded in 2009 by an ex-Googler, to bridge the gap between Thinking-for-Business and Custom Product & Software Development. The organization counts Fortune companies like Facebook, Bloomberg, Microsoft, Siemens, Roche, and Netsmart amongst its clients.
Ideas2IT typically works on innovation-led product engineering projects. The company is best known for leveraging cutting-edge technologies and technical depth.
Learn more at www.ideas2it.com.