In recent times of uncertainties, major concerns for professionals are to be relevant in the market to have a long-lasting career. You never know which new technology would come and affect your career adversely. Few ways to deal with this is to opt for your career wisely or keep an eye on the latest technologies and updating yourself regularly. If you are thinking about new career opportunities that are stable, long-lasting, in great demands, and with high earning potential, a data engineer role would be one of the better options if not the best. Look at the below quote:
“Everything is going to be connected to cloud and data… All of this will be mediated by software.”
- Satya Nadella, CEO, Microsoft.
Clearly, a data engineer is a future-oriented role with immense scope. A data engineer can work in any field across industries. Lack of proficient data engineers has resulted in high demands and increased earning potential. Without dwelling much or getting into a dilemma you should take data engineering courses and land yourself onto a great and successful career boat.
Who is a Data Engineer?
A data engineer is an important part of overall data analytics teams in any organization. Teams consist of different data experts to help management to resolve critical issues on a regular basis or make informed decisions backed with strong data analytics. Data engineers usually work in tandem with other data experts to achieve common goals of transforming data into handy insights. It is a vital role in the data analytics team as other data experts depend on the work of data engineers.
Data engineers primarily focus on transforming data using steps involving assessing, integrating, cleaning, and processing data from all available sources. Since data is available from many sources in bulk, data engineers need to write robust scripts and develop queries to achieve transformation. In other words, a data engineer is responsible to work on infrastructures and data pipelines for smooth data flow across the team and ensure data availability to all data experts in required formats.
Roles and Responsibilities
The roles or responsibilities of data engineers depend primarily on the organization’s needs and may change based on the project scope. If a project is at an initial level, data engineers would work on all aspects of architecture and data pipelines compared to an ongoing project. Here are some of the common responsibilities of the data engineer.
- The data engineer needs to work out data flow architecture in line with the business requirement of the data analytics team.
- While developing the architecture data engineer needs to focus on scalability, flexibility, and fault-tolerance traits.
- Once designed and developed or if already existing, data engineers should ensure to look for ways to optimize the architecture.
- Maintain the existing architecture to avoid failures.
- Designing data pipelines in such a way that it caters to all sorts of data formats and from all kinds of sources.
- A robust system is expected which transforms the data in the format required by data experts across the team. Steps involved would be to collect, filter, process, store, and visualize the data.
- Sometimes data engineers need to optimize or maintain existing pipelines, for that data engineers need to understand the previously existing flow.
- Data engineers would be required to work on improving the system efficiency of overall systems.
- Design, maintain or optimize databases for effective data storage.
- The key to successful data analytics is to report the finding at the right time and in a format that makes it easy to interpret the outcome.
- Dynamic reporting or dashboard creation is one of the important aspects which helps to achieve this.
- A dashboard helps to identify hidden patterns and trends in data sets, which makes raw data more beneficial to an enterprise.
Data Engineer Skills
In order to be a successful big data engineer, candidates need to develop key skills such as scripting in multiple languages, data processing techniques and tools, database handling, etc. Below are some skills listed in detail to help you start with
- Data analysis always starts with data acquisition or collection, data engineers should develop a good understanding of data acquisition systems. Details like work on to build knowledge on incoming data type, format i.e. structured (in the form of a table, files), or unstructured (image, text, audio, or video files).
- Data mining is an important aspect of data analytics as a whole and helps data engineers to extract usable data. This subsequently helps in improving the performance of the overall system.
- Data processing requires strong fundamentals in scripting. Advanced knowledge of programming languages like Java, Scala, Python, or R is desirable. The flexibility to work on multiple programming languages is a highly recommended skill. Data engineers should start with any one of the programming languages and build on to other languages based on interest, time, and capacity.
- Strong knowledge of SQ for data engineers is a must, SQL is quite old and maybe on the verge of extinction, however, the structured query language is still very much alive and thriving. While working in a Big data environment, tools like impala and Apache hive might be required. NoSQL database fundamentals, including Postgres and Cassandra, is a must.
- Workflow management tools such as Azkaban, Luigi, and Airflow are highly preferred for data engineers to include in their armor.
- Data engineers should learn tools such as Hadoop, Spark, MapReduce, Kafka. Engineers should work on tools to gain hands-on experience in the Big data environment and upskill themselves.
- Data storage and handling is one of the core responsibilities of a data engineer. It is highly recommended for data engineers to develop skills for database design, maintenance, and optimization.
In addition, the following skills are preferred
- Since data engineers would work with various data experts in the analytics team, knowledge of statistical tools would be a good addition.
- Machine learning as a subset of Artificial intelligence is a future trend and would be required at various stages of data processing. Data engineers should learn all aspects of machine learning including the latest algorithms.
- Data engineers are required to communicate their work in multiple forums or take inputs from various stakeholders, good communication skill is required.
- Problem-solving is a skill required in general by each and every engineer to suggest a good for critical issues or to enhance data integrity, system efficiency, and reliability.
- Understanding of prescriptive and predictive modeling.
Data engineering surely is the future trend for the next decade and so, it offers immense growth and a rewarding career. This article tried to showcase what a data engineer is expected to do and what skills need to be acquired in order to be a successful big data engineer. If the skill that you have and what is needed for a data engineer has a long gap, you may opt for a good online course in data engineering and bridge the long gap. There are a number of online courses available with dummy industrial projects to upskill you and make you competitive. So don’t wait and opt for an online course and start your journey.