Data Engineering Explained

Data engineers are in high demand globally. In the U.S. alone, an anticipated 26% increase in open data engineer positions each year is expected between 2023 and 2031 — that translates to 143,400 open positions each year!

(Source: Dice Tech Job Report, 2020)

Even more encouraging, the initial anticipated salary range for data engineers in the U.S. spans roughly $71,280 to $127,260 based upon geographic location, educational background, and skill set.

Are you just getting started in your professional life or considering a career switch for your second act? Read on to discover more about data engineering, in-demand data engineers' skill sets, and how you can pursue a career in data engineering.

Table of contents

• What is data engineering?
• Key data engineering skills
• Become a data engineer
• FAQs

What is data engineering?

Data engineering is the discipline which creates data collection, storage, transformation, and analysis processes for large amounts of raw data, structured data, semi-structured data, and unstructured data (e.g., Big Data) so that data science professionals can draw valuable insights from it. In fact, as illustrated below in The Data Science Hierarchy of Needs, data engineers are the literal foundation that enable data scientists to perform their analysis.

(Source: Gigster, 2023)

Data engineering also encompasses data quality and data access assurance. Data engineers must make sure that the data sets from a variety of data sources (e.g., data warehouses, cloud-based data) are complete and clean prior to loading data and beginning data processing tasks. Further, they must ensure that data users (e.g., data scientists, business analysts) are able to easily access and query the prepared data via a variety of data analytics and data scientist-preferred tools.

Key elements of data engineering

Clearly, both the definition and applications of data engineering are incredibly broad. To better understand the discipline, consider the following key elements of data engineering.

(Source: Medium.com, 2023)

• Data extraction/collection: As its name implies, this element involves the creation of systems and processes to extract data of varying formats from multiple sources. This includes everything from structured customer data in relational databases and data warehouses, to semi-structured data such as email and website content stored on a server, and unstructured data including video, audio, and text files stored in a data lake. The variety of data formats and data sources are literally endless.

• Data ingestion: Data ingestion involves data source identification as well as data validation, indexing, cataloging, and formatting. Given the massive amounts of data involved, data engineering tools and data processing systems are often used to speed the ingestion of these large datasets.

• Data storage: Data engineers take ingested data and design the necessary data storage solutions to house it. These solutions include everything from a cloud data warehouse to a data lake, or even a NoSQL (not only structured query language) database. In addition, data engineers can also be responsible for data management within these storage solutions depending on organizational staffing and structure.

• Data transformation: To make data useful for data scientists as they build machine learning algorithms, as well as for use in business intelligence and data analytics, the data needs to be cleaned, enriched, and integrated with other sources. For this reason, data engineers develop ETL (extract, transform, load) data pipelines and data integration workflows to prepare these large datasets for data analysis and modeling. A variety of data engineering tools are utilized (e.g., Apache Airflow, Hadoop, Talend) depending upon the data engineer's data processing needs and the end user's (e.g., data analysts, data scientists) requirements. The final step in data transformation is to load the processed data into systems that enable data scientists, data analysts, and business intelligence professionals to work with it to produce valuable insights.

• Data modeling, scaling, and performance: Creating and defining data models is another key element of data engineering. In the past few years, artificial intelligence (AI) via machine learning models has become a common way to optimize everything from data volume and query load management to overall database performance and scaling infrastructure.

• Data quality and governance: Making sure that data is accurate and accessible is another key element of data engineering. Data engineers create validation rules and processes to ensure that organizational data governance policies are adhered to and data integrity is maintained.

• Security and compliance: Data engineers are often responsible for ensuring security measures prescribed by organizational cybersecurity protocols and/or industrial data privacy regulations (e.g., HIPAA) are met and all systems are in compliance.

Types of data engineers

The range of opportunities for data engineers is a broad one. Within those opportunities, data engineers tend to focus their careers in one of three ways which helps them focus their data engineering skills in their areas of interest.

Generalists

These data engineers are responsible for supporting virtually the entire Data Science Hierarchy of Needs — from data requirements gathering and data collection, to building data pipelines and managing data transformation, to data management and storage, data modeling, data aggregation/labeling, and even simple machine learning algorithms and analyzing data. Commonly, generalist data engineers work with smaller teams and are more focused on data-centric tasks, rather than data system architecture. For this reason, professionals in data science looking to move into data engineering often choose to start as generalist data engineers.

Pipeline-centrists

Pipeline-focused data engineers are responsible for building, maintaining, and automating data pipelines within big data systems. Specifically, they build ways for data to move from one place to another (e.g., data pipelines), focusing on functions in the second and third tiers of The Data Science Hierarchy of Needs (e.g., Move/Store, Explore/Transform). Examples include data extraction, data ingestion, data storage, data anomaly detection, and data cleansing. These professionals also create ways to automate tasks within the data pipeline to improve efficiency, data availability, and lower operational costs. Tending to work for larger organizations, these data engineers work with larger teams that focus on more complex data science projects and often work with distributed data systems.

(Source: addepto, 2023)

Database-centrists

Within larger organizations with significant data assets, database-centric data engineers focus on the implementation, population, and management of a data analytics tool database(s), data analytics platform(s), and other modern data analytics tools used to create machine learning algorithms and AI features (e.g., the Aggregate/Label, Learn/Optimize levels of The Data Science Hierarchy of Needs). These data engineers may also work with data pipelines as they take transformed data and load it via ETL data engineering tools into various data analytics systems, automating processes where possible and optimizing database efficiency. Finally, they may also employ data engineering tools to further enhance data for data scientists (e.g., specialized data sets, automated SQL queries, customized data tools).

Data engineering offers flexibility and options

It's important to note that data engineers can choose to specialize even more deeply than the categories above, if so inclined — focusing on becoming expert in top data engineering tools, building ad-hoc business intelligence solutions, focusing on specific cloud-based data platforms, or even leading data engineering teams, just to name a few of the possibilities. The options are endless!

Further, it's not uncommon for data engineers to switch from being generalist data engineers to pipeline-centric data engineers or database-centric data engineers (or vice versa). Often, as data engineers gain experience and additional skills in certain areas, they will migrate to positions that make use of valuable new skills in the modern data stack (e.g., machine learning, data lakes management, developing top data engineering tools).

Key data engineering skills

Data engineering skills are just as broad and varied as the data engineering discipline itself. The industry and position a data engineer chooses will often dictate the specialized skills required. However, there are commonly used data engineering skills that all data engineers require.

Hard skills (e.g., technical skills)

• Programming language proficiency: Having strong proficiency in a variety of programming languages is critical in the pursuit of a data engineering career. Early-career data scientists often focus on a few key languages (e.g., Python, SQL, NoSQL) and continue learning throughout their careers. Some of the top programming languages used include:

  • Python.
  • SQL.
  • Golang.
  • Ruby.
  • NoSQL.
  • Perl.
  • Scala.
  • Java.
  • R.
  • C.
  • C++.

• Data warehousing: Data engineers are entrusted with the analysis and storage of vast data sets. As a result, understanding database design, query optimization, and schema modeling is also essential. A working knowledge of key data warehouse data engineering tools is also required. Specifically focusing on the second and third tiers of The Data Science Hierarchy of Needs (e.g., Move/Store, Explore/Transform), key data engineering tools include:

  • Amazon Redshift.
  • Google Big Query.
  • Apache Cassandra.
  • Apache Spark.
  • Apache Airflow.
  • Apache Hive.
  • Alteryx.
  • Tableau.
  • Looker.
  • Segment.
  • Fivetran.

• Cloud services: Basic familiarity with cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) is essential, as many large organizations already maintain their data assets in the cloud, and smaller organizations are migrating to the cloud daily.

• Data modeling: Proficiency in data modeling techniques, including designing schemas and data structures for optimal query performance, and data integrity is a data engineering necessity. Such tools as SQL Database Modeler, Apache Cassandra, PgModeler, and DTM Data Modeler are commonly used.

• Artificial intelligence (AI) and machine learning: A basic understanding of AI and machine learning, including familiarity with common algorithms and their applications, is a key element of the data engineering skill set. In addition, familiarity with relevant Python libraries (e.g., Numpty, Pandas, TensorFlow, PyTorch) and experience with Jupyter Notebooks is also important.

• Data pipeline orchestration: Experience with tools like Apache Airflow, Luigi, or cloud-based solutions (e.g., AWS Step Functions) for orchestrating and scheduling data pipelines is important for data engineers.

• Version control: Familiarity with version control systems like Git for managing code changes and collaborating with team members is a key skill all data engineers should possess.

• Automation: The ability to automate repetitive tasks and processes using scripting languages (e.g., Python, Ruby) and tools like Bash scripting helps data engineers streamline data processing while saving time and operational costs.

• Containerization: Proficiency with containerization technologies like Docker and container orchestration tools like Kubernetes for managing and deploying data engineering applications is a desirable skill for data engineers.

• Streaming data: Familiarity with streaming data technologies such as Apache Kafka or cloud-based solutions like AWS Kinesis for real-time data processing is often needed by data engineers working with social media data, scientific sensor data, etc.

• Monitoring and logging: Installation and proficiency with monitoring and logging data engineering tools to track data pipeline performance and database performance, as well as troubleshooting issues, is a necessary skill set for all data engineers.

Soft skills (non-technical skills)

• Problem-solving: The ability to identify and address data-related challenges, troubleshoot technical errors, and optimize data pipelines for efficiency is just one example of how strong problem-solving skills are a requirement for data engineers.

• Empathy: In order to effectively collaborate with internal clients (e.g., data scientists, BI analysts) and design data solutions that meet the needs of data consumers, data engineers must employ a sense of empathy. Specifically, this means understanding that the most expeditious solution for the data engineer may not be the right one for the internal client or end user, and working further to create a solution that meets the needs of all parties.

• Adaptability: With technologies and tools evolving rapidly, data engineers must be willing to embrace new tools, methodologies, and approaches on a daily basis.

• Time management: Projects assigned to data engineers often have tight deadlines, making excellent time management skills a necessity to meeting project milestones.

• Communication: Effective communication skills are essential for collaborating with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet their needs.

• Conflict resolution: As with all work groups, data engineering teams encounter conflicts. The ability to address and resolve conflicts in an objective, respectful, and constructive manner is critical to maintaining team trust, cohesion, and productivity.

• Documentation: While not the most exciting part of the job, documenting processes, creating data pipeline diagrams, and providing code notation is essential for data engineers as it provides other data engineers with information needed for knowledge transfer, troubleshooting, and data system maintenance.

• Presentation skills: While it may not seem like a necessary skill for technical professionals, the ability to explain technical barriers, discuss findings, and present final projects to non-technical stakeholders is critical. Without consistent understanding within stakeholder groups and management, projects can quickly veer off-track, requirements can be misunderstood, and budgetary issues can occur.

• Continuous learning: Data engineering is a rapidly evolving field, so a willingness to stay updated with the latest technologies and best practices is important.

Become a data engineer

Are you ready to become a data engineer? If so, consider the many pathways to begin a career in data engineering to find the way that's right for you.

Degree

While few universities offer a degree in data engineering, they do offer degrees in computer science, advanced mathematics, software engineering, and data science. Many aspiring data engineers pursue a bachelors degree in one of these areas, or even go on to earn a master's degree prior to beginning their career in data engineering.

Career transition

It's fairly common for aspiring data engineers to make a shift mid-career into the discipline. Whether driven by a newly discovered interest or knowledge of the significant demand for data engineers, adults join the data engineering field every day.

One common career transition into data engineering is for those with an existing background and/or career experience in business intelligence or data science to gain more technical expertise and move into the data engineering discipline. However, many aspiring data engineers come from a variety of backgrounds. Here are some of the ways you can build data engineering skills beyond a traditional four-year degree.

Continuing education

Certainly, taking courses to build your data engineering skill set is a good idea. Consider some of these options to determine which is best for you.

• Online self-paced courses: There are a variety of low-cost options to help you gain a general understanding of data engineering (e.g., Udemy) or specific programming languages (e.g., Coursera).

• Boot camps: Boot camps are educational programs that specifically focus on educating students in the practical, functional skills necessary for entry-level employment in their chosen field. Boot camps can be an option for those who need more structure and interaction than self-paced courses and also provide a certificate of completion to show to potential employers. While many boot camps are offered by for-profit corporations, many well-known universities offer boot camps as well (e.g., University of Central Florida). Regardless of the boot camp provider, it's important to carefully research a potential boot camp prior to enrolling (e.g., student reviews, curriculum covered, graduation rates/drop rates). It's also important to note that boot camps can be very expensive, costing anywhere from $7k to $30k.

• Online communities: Many beginning coders rely on online communities for help in answering questions, to practice their coding skills on open-source projects, and to gain access to scripts and tools built by other community members. Some of the most popular online communities include GitHub, Stack Overflow, and Code Project, just to name a few. In addition, many programming languages have their own communities. Some examples include Python, Ruby, and Golang.

Regardless of the path you choose, building a strong skill set in both the hard and soft data engineering skills you'll need to pursue a data engineering career will be an investment in your future you won't regret.