As part of a blog series on the roles and functions of various data teams, in this blog I will go over what the role of a data engineer is and what the job of a data engineer is like in practice.
First, the data engineer role and scope, like other data roles, often changes depending on the stage of the development project and the needs of the company. At different stages of the projects, certain skills requirements are emphasized. These are also great opportunities for skill development.
In some cases, the roles of data engineer, data scientist, and data analyst overlap, and the boundaries between roles can be blurred. However, today, projects and clients seem to have a clearer understanding of the established roles and responsibilities in the data industry than they did even a decade ago.
Below I will go into more detail about what a data engineer’s job is in practice and what technologies and skills a data engineer must master. I will mention some technologies in the blog. Things can also be achieved and implemented with other competing technologies. At DB Pro Service, we focus on Azure and AWS cloud services as well as Databricks, Snowflake, Synapse and Fabric data platforms. At DB Pro Service, you will find senior-level certified experts for these data and analytics solutions.
Although the list of skills of a data engineer is long, it is good to remember that larger projects are implemented and the end results are achieved as a team, which includes not only technical professionals but also business experts. The skills of the data team members complement each other, and the team members share knowledge within the team so that everyone can continuously learn and develop.
Data engineer tasks and important areas of expertise
In simple terms, a data engineer’s job is to bring raw data from different sources into a data platform and cost-effectively transform it into a usable form for different users. Data engineers are professionals who, in addition to technical expertise, must also have business understanding and excellent problem-solving skills. Since the requirements of the job and the field of specialization vary, there is no exact list of skills required to become a data engineer. Most of the skills are learned in practice in projects and by educating yourself.
The skills required to be a data engineer are diverse and encompass both technical and analytical skills. Here are some key areas in which a data engineer should be strong:
- Cloud platform management and architecture
- Integrations between source systems and the data platform
- Design, build and orchestrate data pipelines
- Information platform, database architecture and modeling
- Python and SQL programming skills
- Infrastructure as Code (IaC) practices
- CI/CD and DataOps
- Information security and privacy practices
- Documenting and demoing solutions
- Continuous learning
In the following paragraphs, we will go through some of these different areas of expertise and their contents in more detail.
Cloud platform management and architecture
Data engineers participate in the management and architecture of the cloud platform, for example by designing and implementing the resources, access rights, and data storage and storage solutions required for the different environments of the data platform in the cloud platform. Such solutions include, for example, Data Lake or Event-based solutions. In addition, a separate solution can be utilized for archiving data for rarely used historical data from an analytical data platform. A data engineer manages these entities and understands which solution solves the business need.
Database architecture and modeling
A data engineer plays an important role in defining the database architecture. His or her duties include deciding how and in what format the data will be brought into the data platform. For example, one must assess whether the data will always be rewritten, or whether only new and changed data will be stored.
The data engineer is also responsible for modeling data in different layers of the data warehouse to optimize the data for efficient use. For example, the architecture of a data warehouse can be divided into three main layers: raw data, cleaned data, and a utilization layer. The tasks of these layers are as follows:
Raw data layer:
This layer is the first stage of a data warehouse, where data is stored in its original, unprocessed form. The raw data layer acts as the “source of truth,” preserving the integrity of the original data. The data engineer’s job is to design how the data will be stored and how to ensure its integrity so that it can always be retrieved when needed.
Cleaned and organized data layer:
In this layer, raw data is processed and cleaned for analysis. In this phase, the data engineer handles cleaning and editing operations, such as correcting data errors, aligning value distributions, and merging data from different sources. This layer stores a processed version of the data that is ready for analysis, but not yet tailored to a specific user group or business need.
Utilizer layer:
This layer is designed specifically for the needs of end users, such as data analysts and business experts. In this layer, the data engineer models data into data structures suitable for different use cases. For example, data aggregation, adding dimensions, and data segmentation are typical operations in the utilization layer, where data is easily modified for use in reporting and analytics. The requirements and model for the utilization layer often come from the utilization users. The data engineer’s task should at least implement automation and orchestration for transferring data to the data model.
Integrations and ETL processes
The most important tasks of a data engineer include the design and implementation of integrations and ETL processes. Data integration means collecting data from different sources and combining it into a unified whole. A data engineer is responsible for ensuring that data is consistently imported from different systems – such as CRM, ERP and IoT systems – into a data warehouse or data lake.
Combining data sources:
Data integration requires expertise with different sources, such as SQL databases, APIs, and external data sources. A data engineer designs and builds integrations that enable data to be transferred to centralized platforms.
Design, build and orchestrate data pipelines
Data pipeline design determines where and how data is collected and in what format it is stored in the data platform. During the construction phase, data pipelines are technically implemented: Data is collected, processed, and loaded into the data warehouse.
Utilizing ETL tools:
Data engineers utilize various ETL tools that facilitate data migration, manipulation, and loading. Examples include Azure Data Factory and AWS Glue – cloud-based ETL tools that enable data migration and manipulation directly in the cloud.
Orchestration achieves flexibility and efficiency in the following ways:
Timing and scheduling:
Orchestration tools like Azure Data Factory and AWS Step Functions enable scheduling, allowing data pipelines to be executed automatically at specific intervals (e.g. once an hour or once a day) or on an event-based basis, reducing the need for manual work and ensuring data is up-to-date.
Concurrency and dependencies:
Orchestration can be used to determine which steps in a data pipeline can be executed in parallel and which must be run sequentially due to dependencies. For example, data collection from multiple sources can be done simultaneously, but the editing and cleansing phase only begins after all the necessary data has been collected. Parallelism improves performance and reduces the overall runtime of the data pipeline.
Data pipelines can also be built to be fully real-time, where data flows from sources to analysis or storage as soon as it arrives without any delays. Real-time data pipelines enable a continuous data flow, where new data is processed and transferred to target systems as soon as it is created. This is especially useful in applications that require up-to-date information, such as monitoring data from IoT devices.
It is important to remember that real-time data transfer requires continuous computing capacity and a large number of resources, as data is processed and transferred continuously. This increases the costs associated with the use of cloud services and infrastructure.
Information security and privacy practices
A data engineer’s key tasks related to information security and privacy practices include:
- Masking and pseudonymization of sensitive data where necessary
- Managing and renewing various keys and permissions for integrations
- Defining firewall openings and IP restrictions
- Creating and managing a data platform and permissions
The impact of the project phase on the tasks of a data engineer
The phase of the project significantly affects the tasks of a data engineer. In the early stages of a project, tasks focus more on planning, auditing, and drafting various policies, such as information security.
This is followed by the development phase, where tasks focus on building and testing the platform, integrations, data pipelines, and databases. In larger projects, the development phase usually continues at a slower pace during the maintenance phase, when the platform and data warehouse have reached the production phase. In this case, the data engineer’s tasks also include monitoring the data platform, integrations, and data pipelines, as well as operational activities, such as identifying and correcting data quality issues and failed runs.
In addition, the data engineer optimizes existing data pipelines and functions so that the data platform functions optimally.
Could we help build and develop a data platform?
DB Pro Services offers top-notch data engineers for Azure and AWS platforms, as well as Databricks, Snowflake, Fabric, and Synapse data platform suites. Contact us and we will help you and your organization leverage data effectively and compete!
Robin Aro
Head of Services | Lead Data Engineer robin.aro@dbproservices.fi
DB Pro Services Oy Read also: What is a Data Analyst? What is a DBA?
