With data being a highly valuable asset in today’s data-driven world, roles requiring expertise and knowledge on data have been on the rise as institutions hire data experts to help make the most out of the data obtained.
This article looks into data engineering, its roles and responsibilities, differences between data engineers and data scientists, skills needed, why one would consider a data engineering career path, where data engineers can work, and why companies need to hire data engineers.
Defining data engineering
Data engineering is a field focused on designing and building systems used in the collection, organization, and processing of data. When the data is collected, it is often in a raw and unstructured format that requires the input of a data engineer to transform it so it can be used in analysis and operations.
In an era where data is considered to be a valuable asset, data engineers are needed to help ensure data is available whenever and wherever it is needed. With businesses generating a lot of data, these professionals are tasked with preparing and optimizing the data for analysis and operations.
What are the roles and responsibilities of data engineers?
Some of the roles and responsibilities of data engineers include:
Data pipeline management and designing: Data engineers are tasked with designing and building automated ETL pipelines, which help ensure that data flows smoothly from sources to data warehouses or lakes.
Managing data infrastructure: As organizations increasingly accumulate more data, engineers help select and manage storage architectures that can handle the growing datasets efficiently and maintain performance and scalability.
Data modelling: Involves creating data models and schemas that make storing and retrieving data efficient.
Optimizing performance: This is achieved by improving query speeds, cost management, and streamlining the infrastructure.
Ensuring data security and compliance: It is the responsibility of a data engineer to ensure that policies on data access and usage are implemented.
Data engineering vs data science
The roles of data engineers and data scientists are often confused. Although they work together, they have differences in aspects such as goals, tasks, skills, and the tools they use.
Before delving into the differences, let’s look at what data science is. Data science is a multi-disciplinary field that integrates maths, artificial intelligence, analytics, and machine learning.
Goals: While data engineers aim to build infrastructure for data collection and make data accessible and usable, data scientists aim to use data to extract insights and predict outcomes while utilizing machine learning, thus enhancing and improving the decision-making processes.
Tasks: Data engineers are mainly tasked with building ETL pipelines, warehousing, and modelling, while data scientists are tasked with analytics, machine learning, and visualization.
Skills: Data engineers need skills in programming, databases, ETL, and architecture, while data scientists need skills like mathematics, statistics, and machine learning.
Tools used: Data engineers mainly use tools such as Python, Apache Spark, Kafka, Airflow, and SQL, while the main tools for data scientists are Python, R, TensorFlow, scikit-learn, Jupyter, and Notebooks.
Data engineering is the backbone of data science, analytics, and machine learning, as it helps make life easier for other data specialists. Data engineers will build the infrastructure to handle data, and the data scientists and analysts will use that data to gain insights crucial for making informed decisions.
Data engineering skills
Programming skills. Knowledge of programming languages such as Python, Java, or Scala is needed.
Database knowledge. Data engineers need to understand relational databases like SQL for structured data and NOSQL for unstructured data, which are essential in storing and managing data.
Familiarity with ETL (extract, transform, load). Data engineers deal with different data sources from which they have to clean the data and load it to the required destinations, thus they need to know how to use tools such as Apache Airflow.
Cloud computing knowledge. The expansive use and adoption of cloud platforms like AWS and Azure to store data necessitates that data engineers have cloud computing expertise.
Big data technologies. To handle huge data sets, it is essential to be familiar with frameworks such as Apache Kafka, Hadoop, and Apache Spark, which largely help examine complex data sets.
Problem-solving and analytical skills. Data engineers often work with complicated systems where they encounter problems that require them to be quick and effective.
Why consider a data engineering career?
High demand: Opportunities for data engineering continue to be on the rise as organizations increasingly become data-driven.
Lucrative opportunities: Data engineers are among the highest-paid professionals in the tech field for their expertise and skills.
Diverse opportunities: Work opportunities are not limited since the profession is spread across fields such as finance, retail, technology, and health care.
Diverse and transferable skillset: since data engineering combines skillsets in database management, cloud technologies, and systems architecture, it opens doors across different industries such as data science, data analytics, and software engineering, among others.
Basis for AI: Data engineers create pipelines and the infrastructure that powers AI and predictive analysis, without which analytics and machine learning would not be possible
Where do data engineers work?
Data engineers work in different industries such as media, finance, technology, education, healthcare, retail, and government. Let’s dive into some of their responsibilities in these sectors:
E-commerce and retail - A data engineer can build recommendation systems by processing a client’s behaviour, transactions, and product data. Most importantly, the data engineer will help streamline operations by enabling real-time analytics for inventory management, logistics, and forecasting.
Healthcare - He/she may integrate and process patient data to support diagnostics and personalized treatment, which helps to improve patient care and research.
Finance - A data engineer may create pipelines that can help in fraud detection, risk assessment, and customer analytics. The engineer will also play the critical role of ensuring data quality and integrity are maintained, which is critical for performing audits, filings, and making strategic decisions.
Government - With the government, data engineers undertake responsibilities such as ensuring data privacy and compliance, and integrating census, tax, and public service data.
Media - The data engineer can optimize content delivery, analyze viewer engagement, personalize user experiences, and support recommendation systems.
Why should companies hire data engineers?
Companies and institutions certainly need data engineers, as they help enable businesses to have a strategic advantage as well as utilize the full potential of their data in the following ways:
Data security. With data being a valuable asset and in an age where data breaches and privacy concerns are dominant, a data engineer will implement secure and reliable data systems, ensuring data safety and accessibility.
Cost saving. Data engineers help reduce costs by providing solutions tailored to the business needs. By optimizing data storage and processing, they also help reduce cost and improve resource utilization.
Scalability. Companies increasingly face large data volumes, and data engineers help ensure the data infrastructure remains flexible with the business needs. Scalability is especially crucial for businesses facing high growth and needing data solutions to keep up with the growth.
Improved analytics and insights. Data engineers create well-structured data pipelines and storage solutions that help data scientists and analysts access and analyze data easily, leading to quicker insights and timely decision-making.
Data quality: High data quality is crucial since inaccuracies can easily lead to flawed analysis and misguided business decisions. Data engineers will validate and cleanse the data and monitor systems to identify and fix irregularities in the datasets.
Conclusion
Data engineers are beyond technical specialists; they are business enablers, as their expertise in data plays a crucial role. By ensuring the required data is available at the right time and in the right format, they help institutions move faster, make better decisions, and remain competitive.
Data volumes will undoubtedly continue to grow, and so will advancements in technologies like AI, cloud computing, and big data, which further fuels the need for data engineers. It is the companies that invest in building strong data engineering teams that will be positioned for long-term success.
