What Is Data Engineering: Skills, Salary, & Tools

From small startups to large enterprises, businesses generate vast amounts of data every minute. According to Statista, global data generation is expected to exceed 180 zettabytes by 2025. This rapid growth highlights the need for data engineers who can help manage, analyze, and extract insights to make strategic business decisions. As demand for data engineers increases, you can stand out by building a strong skill set that enhances your career prospects. 

If you are looking to advance your career in data engineering, you’re in the right place. 

This article will cover the roles, responsibilities, skills, and tools essential for success in data engineering.  

What Is Data Engineering?

Data engineering is a process of designing, building, and managing data infrastructure that helps gather, organize, and transform raw data at scale. Through this process, you can ensure that the data is fully prepared and ready for analysis. When the transformed data reaches your data scientists and analysts, they can easily query it to uncover insights for improved business intelligence and decision-making. 

Since data engineering is the foundation of data science, fields like machine learning and deep learning depend on it to process information effectively. 

Why Is Data Engineering Important?

Here are key reasons why data engineering is crucial:

Enables Data-Driven Decision Making

Data engineering streamlines the process of transforming your data into a highly usable state. Such well-organized data allows your data scientists to make informed business decisions based on real insights rather than speculations. 

Enables Data Integration

Data engineering can help integrate data from various sources, such as databases, applications, and external systems, into a single location. This integration allows for a comprehensive view of your business operations, which is crucial for analyzing trends and generating reports. 

Improves Data Quality and Reliability

Data engineering focuses on cleaning and validating data for precision, consistency, and reliability. Only high-quality data can help generate accurate insights and analytics. To improve data quality, you can remove errors, fill in missing values, and standardize formats. 

Enhances Data Accessibility

Data engineering ensures that data is easily accessible to those who need it. By setting up data pipelines, you can make it possible for analysts and other professionals to retrieve and use data quickly. Enhanced data accessibility allows your organization to speed up data analysis and reporting, leading to faster insights. 

Ensures Data Security and Compliance

The data engineering process allows you to implement security measures like access controls or encryption to protect data from unauthorized access and breaches. Its data management practices adhere to established regulatory compliance like GDPR and HIPAA to help maintain privacy and avoid legal issues.  

Supports Advanced Analytics and AI/ML

Transforming large datasets into a unified format through the data engineering process supports advanced analytics. This standardization also allows AI and machine learning systems to train algorithms, build predictive models, and generate accurate insights.

Core Concepts in Data Engineering

Let’s understand the key data engineering concepts that can help manage large datasets effectively:

Data Lifecycle Management

Data lifecycle management is a systematic approach that helps oversee the entire journey of data from its generation to its destruction. It includes different phases for collecting, processing, utilizing, or eventually deleting data. Each phase must ensure the data remains useful for decision-making and analysis.

Data Architecture

Data engineering allows you to build and maintain an architecture that aligns with your organization’s needs for handling data. To do so, you must choose and implement a cloud-based or on-premise platform that lets you host your business data at scale. Careful planning in implementing an architecture can help with efficient data management and accessibility. 

Data Modeling

Data modeling allows you to design a schema that provides a blueprint of how data elements are organized and how they interact with other elements. Such schema is useful for building databases with consistency in your data architecture. Common data modeling types include hierarchical, network, entity-relationship, and relational. 

Data Integration

Once the architecture and model are ready, data engineering lets you consolidate data from different sources into a unified system without transformations. Depending on your schema flexibility, cost, and performance, you can use any storage solution, such as BigQuery, Amazon S3, Oracle, MongoDB, SQL Server, and more. 

Data Pipeline

Data engineering enables you to create a pipeline, which involves a set of processes to move data quickly from a source to a target system. Automating these processes can help free up your analysts to concentrate on using data in the target system for effective analytics and decision-making.

Data Warehousing

Data warehousing is about centralizing data from various sources with or without transformations. It is mainly designed for querying and analysis, making it easier to generate reports and insights from large data volumes. Common examples include Snowflake, Amazon Redshift, Google BigQuery, and more. 

ETL

ETL, or Extract, Transform, Load, is a set of processes involved in creating a data pipeline. It helps extract data from sources, prepare it into a consistent format, and load it into a data warehouse. This process allows for fully preparing data for detailed analysis and reporting.

Who Is a Data Engineer?

A data engineer is an IT professional whose primary focus is on transforming data into high-quality information for analytical or operational purposes. A data engineer can be any of one of the following:

• Generalist: Generalist data engineers typically work in smaller teams and focus more on data-centric tasks like collection, processing, and modeling rather than data architecture. 
• ‍Pipeline-Centrist: Pipeline-centric data engineers specialize in creating, maintaining, and automating data pipelines within large-scale data architectures.
• ‍Database-Centrist: Database-centric data engineers work in larger organizations to help implement and manage databases, data warehouses, or other analytical platforms used for creating machine learning and AI models. 

When you become a data engineer, you must perform the following tasks:

• Design and build a data architecture capable of data storage and processing, often using cloud platforms like AWS, Azure, or GCP.
• You can develop data pipelines that allow you to integrate data from various source systems, cleanse, and load it into data warehouses or lakes for detailed analytics.
• Collaborate with data science teams to enhance data transparency and enable your businesses to make reliable decisions.  
• Ensure that the data is accurate, consistent, and accessible throughout the data engineering lifecycle.  
• Verify the security measures prescribed by your organization and regulatory compliance like HIPAA are already in place.  

Data Engineering Skills Engineer Should Have

Data engineers must possess a specialized skill set across various tools and platforms to work with data effectively. To grow your career as a data engineer, you should build the following data engineering skills: 

Programming

Proficiency in programming is essential to the data engineering role. You should be very skilled in languages like Python, R, Java, or Scala, which are commonly used for building and automating data pipelines. To write efficient, scalable, and maintainable code for data engineering tasks, you must learn, practice, and develop strong programming skills.  

SQL and Database Management

SQL (Structured Query Language) is a non-procedural language that allows you to interact with relational databases like MySQL, Oracle, and more. It can be considered the backbone of database management as it provides a standard way to create, read, update, and delete data effectively. As a data engineer, proficiency in writing complex queries using SQL can help easily manage and optimize your organization’s datasets. 

Big Data Technologies

Big Data technologies are beneficial for allowing you to handle and process large-scale data by distributing workloads across multiple nodes. Common big data technologies include Apache Hadoop, Apache Kafka, and more. Understanding how to leverage big data technologies enables you to build scalable data pipelines and perform complex transformations and analyses on large datasets. 

Cloud Platforms

Cloud platforms like AWS, GCP, and Microsoft Azure are crucial to modern data engineering. You can move your organization’s on-premise data centers to cloud platforms for their scalable infrastructure and comprehensive set of tools for data storage, processing, and analysis. Familiarity with these cloud services allows you to design and deploy cost-effective data solutions that can grow with your organization’s needs. 

Data Warehousing

Data Warehousing involves storing vast amounts of structured, semi-structured, and unstructured data from varied sources in a data warehouse. You can then query and analyze the processed information in the warehouse for greater business intelligence and improved decision-making. Cloud-based data warehouse platforms like Amazon Redshift, Google BigQuery, or Snowflake can help with effective data warehousing.

ETL Tools and Frameworks

ETL (Extract, Transform, Load) tools and frameworks are fundamental in data engineering to help you automate the data integration process across different systems. Data movement and replication tools like Airbyte can ensure that you can build reliable, automated data pipelines to support your organization’s integration needs. 

Key Responsibilities of a Data Engineer

Here are the key responsibilities of a data engineer:

Designing Data Infrastructure

Designing an infrastructure for storing and managing your data is essential. This includes choosing the right databases, servers, and storage solutions to suit your organization’s needs. Good infrastructure can help store and access data quickly and securely. 

Developing Data Pipelines and Workflows

Developing data pipelines and workflows involves creating automated processes that enable data migration from one place to another. These pipelines can help reduce manual intervention, saving time and reducing human errors. 

Ensuring Data Quality and Integrity

The collected data can be structured, semi-structured, or unstructured and requires cleansing to transform it into a usable format. The cleansing includes checking for errors, removing duplicates, and validating data to ensure it meets the needs of the target system. Maintaining high data quality can help make accurate, reliable, and useful business decisions. 

Optimizing Data Storage

You can optimize the data storage by choosing the right type of storage system and organizing data so it can be accessed quickly. You might decide between using a data warehouse for large-scale analytics or a data lake for storing raw, unstructured data. Optimizing storage based on your needs helps to reduce costs and improve the speed of data retrieval and processing.  

Implementing Data Governance

Implementing data governance involves defining policies on who has access to the data, how it can be used, and how to comply with regulations like GDPR. These policies allow you to protect data from unauthorized access. 

Data Engineering Tools and Technologies

Let’s look at some of the data engineering tools and technologies:

Databases

Databases are systems that allow you to store, manage, and retrieve data. The most popular ones are:

• Relational Databases: These databases help you store data in structured tables with columns and rows. Each table represents an entity, and relationships between entities are managed through foreign keys. Relational databases use SQL, which allows you to query and manage the data. MySQL, PostgreSQL, SQL Server, and Oracle are some of the examples of relational databases. 
• ‍NoSQL databases: NoSQL databases let you handle semi-structured or unstructured data with flexible schemas. Unlike relational databases, NoSQL does not use tables with rows and columns. Instead, it offers various data models, such as document, key-value, column-family, and graph-based. MongoDB, Apache Cassandra, and Apache CouchDB are a few examples of NoSQL databases. 

Data Warehousing Solutions

Data warehousing solutions allow you to consolidate large datasets from disparate sources into a centralized repository. You can then query the aggregated information for detailed analytics and reporting. Here are the two most popular data warehousing solutions:

• Amazon Redshift: A fully managed, scalable data warehouse service in the AWS cloud. Its massively parallel processing (MPP) architecture enables you to deliver faster query performance at a large scale. Redshift uses SQL to help you analyze structured and semi-structured data in data warehouses, data lakes, or operational databases.  

• BigQuery: BigQuery is another data warehouse solution offered by Google Cloud that lets you transform large datasets into valuable insights. Its built-in machine learning services allow you to develop and execute ML models for your data in BigQuery. 

Big Data Processing Frameworks

Big Data frameworks are tools that enable you to store, process, and analyze large amounts of data efficiently across distributed systems. Here are two big data processing frameworks: 

• Apache Spark: A fast, scalable multi-language engine that supports batch and real-time data processing. Using Apache Spark, you can perform exploratory data analysis on petabyte-scale data without downsampling, allowing you to discover hidden patterns or trends. 

• Apache Flink: An open-source, distributed processing engine for stateful computations over batch and stream datasets. It allows you to process the data with low latency, facilitating real-time insights. 

ETL/ELT Tools

ETL (Extract, Load, Transform) and ELT (Extract, Load, Transform) tools enable you to automate the data integration process. In ETL, data is transformed before loading, while in ELT, data is transformed after loading into the destination. Here are examples of ETL/ELT tools:

Airbyte

‍Airbyte is an ELT data integration and replication platform that provides over 350 built-in connectors to help you automate the data migration process. If you cannot find a connector that suits your integration requirements, you can create one using the Connector Development Kit. With dbt integration, Airbyte allows you to create and apply custom data transformations to suit your destination needs.

Let’s have a look at a few key features of Airbyte:

• Modern GenAI Workflows: Airbyte allows you to streamline your AI workflows by loading semi-structured or unstructured data directly into vector store destinations like Milvus, Weaviate, Pinecone, and more. You can perform the data integration process in a single operation with Airbyte’s integrated support for RAG-specific transformations like LangChain-powered chunkings and OpenAI-enabled embeddings. ‍
• Developer-Friendly Pipeline: PyAirbyte is an open-source Python library that lets you build custom data pipelines. With PyAirbyte, you can interact with the Airbyte connectors programmatically to extract data from various sources within your Python workflows.‍
• Open-Source Version: Airbyte also offers an open-source version that enables you to host your Airbyte instance locally using Docker or on a virtual machine. This version allows you to use most of Airbyte's features, including 300+ connectors, column selection, all user interfaces, and more. ‍
• Data Security: Airbyte supports credential management and encryption-in-transit to secure your data migration process. It also has ISO 2700, SOC 2 Type II assessment, and GDPR compliance certifications for safe data management throughout the integration.  

AWS Glue

‍AWS Glue is a serverless ETL service offered by Amazon that allows you to simplify the data preparation for quicker analysis. It offers 70+ pre-built connectors that help you effortlessly extract from multiple sources, transform, and move data into your data lakes. You can visually or programmatically build, monitor, and run ETL pipelines using AWS Glue.  

Here are a few key features of AWS Glue:

• Machine Learning Services: AWS Glue uses ML-powered anomaly detection algorithms that allow you to identify inaccuracies or unexpected patterns within your data. Resolving these issues can help ensure more accurate business decisions.‍
• Interactive Sessions: AWS Glue offers interactive sessions that enable you to work directly with the data in your AWS cloud. You can integrate, standardize, and explore the data using tools like Jupyter Notebook.
• ‍Data Catalog: AWS Glue Data Catalog serves as a central repository that lets you store metadata in tables related to your organization’s datasets. You can leverage it as an index to the location and schema of your data sources.  

Data Visualization Tools

Data Visualization tools are software applications that help you create graphical representations of data to uncover insights and support decision-making. Let’s look at two data visualization tools:

• Tableau: Tableau is a business intelligence and analytics software that enables you to see and understand the data. It allows you to easily connect to databases, create visualizations with drag and drop, and share them with your team or customers. 

• Power BI: Power BI is a scalable, unified platform for enterprise and self-service business intelligence. Its drag-and-drop canvas or Microsoft’s modern data visuals help you to create interactive dashboards and reports. 

How to Get Started in Data Engineering?

Here are some essential steps to help get started in the data engineering field:

• Earn a Bachelor’s Degree: While having a degree is not strictly necessary to become a data engineer, some companies may hire candidates with at least a bachelor’s degree. If you aim to pursue a degree to advance your career in data engineering, consider majoring in data science, computer science, or software engineering. ‍
• Develop Your Skills: To build your skills, learn the fundamentals of data engineering lifecycle management and master coding languages. A strong foundation in databases, ETL tools, data analytics, machine learning, and big data tools can also help you succeed in data engineering. Check out the 13 YouTube channels that will help develop your data engineering skills. 
• ‍Get Certified: Obtaining a certification can validate your skills to employers and help you upgrade your knowledge. Some popular certifications include Google Professional Data Engineer or Associate Big Data Engineer. 
• ‍Build Your Portfolio: Create a portfolio to showcase your data engineering projects, whether completed independently or as part of coursework. You can use a portfolio website, LinkedIn, or GitHub to display your work and capabilities to recruiters, hiring managers, and employers. ‍
• Begin with an Entry-Level Position: Start your career with entry-level positions such as database administrator or database developer to gain relevant skills and industry experience. These positions will help you build the foundation needed to shift into more advanced data engineering roles. ‍
• Stay Updated: Join data engineering communities, attend webinars, and participate in active forums to stay current with industry trends and advancements.

Where Is Data Engineering Headed?

As businesses generate more data than ever, data engineering is focused on improving how data is managed and used. This includes making data processing faster and more efficient by leveraging advanced tools and technologies. 

One big trend is the shift to real-time data processing. Companies want insights as soon as data is collected, so data engineering can help build pipelines that can handle this need quickly.

AI is also becoming a crucial part of data engineering, helping to automate tasks and improve accuracy. 

Another important focus is data governance. As data regulations become stricter, the data engineering process can help ensure that data is secure, accurate, and compliant with laws. 

Data engineering will continue to grow and adapt, with more focus on cloud computing, big data tools, and collaborative work with data scientists. 

Summary

Data engineering is a dynamic field driven by the need to manage increasing data volumes and extract value from it for quick analytics. As your organization continues to priortize data-driven decision-making, the demand for skilled engineers is set to rise for transforming raw data into valuable insights. By acquiring the necessary skills, staying updated with the latest tools, and gaining practical experience, you can build a successful career in this evolving industry. 

FAQs

Is data engineering a coding job?

Yes, data engineering requires strong programming support for building and maintaining data pipelines.

What is a data engineering salary?

According to Payscale, an entry-level data engineer with less than a year's experience can expect to earn an average annual salary of $79,562. Those with 1-4 years of experience make about $91,989. Mid-career professionals with 5-9 years earn $111,568, while those with 10-19 years average $124,313. As you gain experience as a data engineer, your salary typically rises. 

Is data engineering a stressful job?

Data engineering can be stressful due to tight deadlines, complex problem-solving, and high responsibility for data accuracy and availability. However, if you are passionate about data engineering, you might enjoy the new challenges that come with the data.

Can a non-tech person become a data engineer?

It is challenging but possible for a non-technical person to become a data engineer with proper training, experience, and self-learning skills in data-related tools and concepts. 

Is Data Engineering a lot of math?

Data engineering involves some math, mainly for data analysis, but it is less math-intensive compared to data science. 

What are the entry requirements for data engineering?

Entry requirements include a degree in computer science or a related field, proficiency in programming languages, SQL, cloud platforms, ETL/ELT tools, and level of experience.