Data engineering transforms raw data into readable forms that can be analyzed and reported.
If that sounds like a simple job, consider that your data comes from many sources and gets stored in various formats. Also, there’s probably a lot of it.
All that data needs to stream from those sources into a central repository and then be converted into a uniform format.
Then it needs to be cleaned, stored, analyzed, and made visible to the teams and executives who will use the data to better steer the business’s direction.
But according to research by Seagate, most companies only leverage 32% of the data available to them.
Data engineering is a complex field involving many roles, each with unique responsibilities and areas of expertise.
What follows is an overview of several data engineering roles and what each role is responsible for. Your organization likely won’t require every single one of these roles, and you’ll notice a lot of overlap between them.
Business Intelligence (BI) Developer
Business intelligence (BI) covers all the technologies, applications, and practices involved in collecting, analyzing, and presenting your business’s data. The main goal of BI is to give you valuable information to improve your decision-making processes and day-to-day operations.
Business intelligence developers make your business data understandable. They identify the company’s reporting needs and then transform raw data into detailed insights you can use to make better business decisions.
Business Intelligence Engineer
BI engineers work alongside BI developers and analysts and have the skills of both. They create and design business intelligence systems with the end goal of making data accessible to analysts.
BI engineers deliver clean and comprehensible data for analysis and the creation of key performance indicators (KPIs). In addition, they develop data pipelines, identify solution strategies from datasets, design and implement reporting and analytics solutions, and other similar activities.
Database Administrator (DBA)
The role of the database administrator isn’t new, but the responsibilities have changed in the era of Big Data. Fundamentally, however, they design, implement, administer, and monitor your data management systems to ensure data consistency, quality, and security.
Database administrators may also manage the installation, configuration, monitoring, maintenance, and performance improvement of your databases, data warehouses, data lakes, and so on. In addition, they may install and maintain your physical database servers.
DBAs are frequently in charge of optimizing database security, setting and maintaining database standards, and managing database access. They keep your database tuned and oversee all database applications. When database errors occur, they are the ones that do the troubleshooting. DBAs are often an on-call role to provide support.
Your DBA may also keep their finger on the pulse of emerging database technologies, recommending which new technology might benefit your organization. And depending on other roles in your business, they might be charged with creating and managing database reports, visualizations, and dashboards.
Data analysts are experts in data manipulation and business intelligence (BI). They examine relevant data sets and create reports and dashboards to help identify trends and other valuable business information.
Data analysts sift through data and draw conclusions, communicating their findings using data visualization, storytelling, and other techniques. They often spend their time performing routine analyses and generating regular reports.
Data architects define data standards and principles and translate business requirements into technical specifications. They’re also responsible for visualizing and designing your company’s enterprise data management framework. This framework defines how your business gathers, secures, uses, stores, and purges the data it collects.
Data architects define your business’s reference architecture, creating a pattern others can follow to develop and improve your data systems. They also describe your organization’s data flows to identify which areas of your business generate data and which require data to function. Additionally, they outline how to manage your data flows and how data changes in transition.
Data engineers design, build, and maintain the infrastructure necessary for collecting and storing your data.
A critical part of that infrastructure is your data pipeline, which brings data from different sources to a central repository, such as a data warehouse, data lake, data lakehouse, or data mart. Data engineers create and maintain the data pipeline.
They are also responsible for processing and storing your data in a manner that allows for rapid access and analysis. A data engineer integrates, consolidates, cleans, and structures your data so it can be used in analytics applications. They are also responsible for your data’s security and integrity.
Depending on the size and complexity of your company’s data, you may employ different types of data engineers:
- Generalists typically perform end-to-end data collection, intake, and processing. They may be more skilled than most data engineers, but know less about systems architecture.
- Pipeline-centric engineers usually handle complicated data science projects across distributed systems.
- Database-centric engineers implement, maintain, and populate analytics databases. Companies with data distributed across multiple databases often require these engineers.
Data modelers focus on identifying your business’s needs and creating data models to fit those needs. They have a deep understanding of data flows and can identify data solutions to help improve your business activities.
Data modelers also define your data modeling and design standards. They develop naming conventions and data coding standards that ensure the consistency of their data models. Additionally, they evaluate data models and databases for variances and discrepancies, and optimize your data systems.
Finally, data modelers reverse engineer physical data models from databases and update local and metadata models.
Data Quality Engineer
Data quality engineers ensure the data you use is accurate and consistent. They frequently work with different databases, applications, and other systems to ensure that all your data is organized and accessible.
Part of a data quality engineer’s job may be to develop new methods or processes for improving the quality of your data. Such measures might require building new software tools or establishing new procedures to verify the accuracy of new data and flag issues needing a human review.
Data scientists use artificial intelligence and deep learning frameworks to formulate advanced insights from layers and data streams. They can also build operational, self-sustaining forecasting algorithms.
Data scientists design new ways to store, manipulate, and analyze your data. While a data analyst interprets your data, a data scientist invents better ways of capturing and analyzing the data.
Machine Learning Engineer
Machine learning engineers combine software engineering capabilities with machine learning knowledge. They focus on the engineering aspects and leave the algorithm-building to data scientists.
Machine learning engineers research, design, and build AI-powered analysis tools. They have extensive knowledge of artificial intelligence, machine learning, and deep learning.
Knowledge Is Power
If knowledge is power, data engineering professionals run the power plant. They create and use technologies that turn raw data into valuable business insights.
Unless you operate at the enterprise level of business, your organization will not need every role discussed above. However, knowing what these roles do may lead you to fill some gaps in your data analysis process.