A data engineer is responsible for extracting, compiling, and analyzing large amounts of data from disparate sources. In today’s technologically driven world, over 2.5 quintillion bytes of data are created every single day, all organizations require employees who can make sense of huge volumes of data. Data engineers play an important role in all kinds of industries, and are often highly sought after by employers.
What Does a Data Engineer Do?
At their core, data engineers are responsible for handling all of an organization’s data. This can include collecting it, processing it, storing it, and making sense of it. They need to know how to manipulate large amounts of data quickly in order to run effective experiments. A data engineer uses technology to find insights within massive amounts of data.
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What Is a Data Engineer’s Salary?
According to Payscale, an entry-level data engineer with less than one year of experience can expect to earn an average total compensation (including tips, bonus, and overtime pay) of around $77,000. Conversely, a late-career (20 years or more) data engineer earns an average total compensation of $115,000. Professionals can expect to see the biggest salary bump once they’ve got at least five years of industry experience, moving from an average of $88,000 for one to four years’ experience to $104,000.
Data Engineer Roles
As the world continues to become more data-driven, data engineers are becoming more important to businesses of all sizes. While there are various titles used to describe data engineers, their roles generally fall into three categories.
These types of data engineers usually function in a small team. They often wear multiple hats in order to help keep things running smoothly. Generalists may be responsible for installation tasks, production environments, ETL (extract, transform, load) jobs, archiving unneeded historical information, storing it in an offsite location, or performing disaster recovery and business continuity activities. A generalist engineer collects, organizes, and manages data, as well as setting up procedures for ensuring data remains accurate and safe.
A pipeline-centric data engineer often works in a midsize enterprise. If you have many pipelines producing many types of data, your pipeline engineers will need to specialize in understanding how those pipelines work and which tools are being used within them. Further, these engineers need to understand how data will be consumed later down the line. They often work with data scientists to make use of collected data.
Some companies only perform real-time analytics, others store all their data in data warehouses, and others use both storage systems. Others simply archive older entries while new ones streamline into memory. Regardless of the use case, DB specialists must know how to optimize space usage without sacrificing speed; they help prevent analysis paralysis.
What Are Data Engineer Responsibilities?
The data engineer’s responsibilities vary greatly depending on the nature of their employer. For example, they might be responsible for transforming and storing data, or analyzing that data to determine patterns or trends. However, many times data engineers are tasked with combining multiple datasets to create an improved version of what’s already available. They also help other departments extract valuable information from large volumes of data, and then act on that information.
- Metadata management: When dealing with large amounts of data, metadata plays an important role in locating and managing files.
- Creating relational database schemas: Data engineers must decide which tables need to be included in database schemas, how these tables relate to each other, and whether additional indexes or constraints should be added for queries.
- Implementing ETL: A full-time task for some data engineers is extracting information from one set of systems and loading it into another.
- Building reports and dashboards: There will always be reporting needs for any business. Data engineers may tasked with setting up applications within database tools to visualize large sets of customer and product data to non-technical teams.
- Setting up roles and privileges: Providing appropriate database access privileges to team members is also essential when there are multiple users working together on large datasets.
- SME for BI systems: The aim of a data engineer isn’t just finding solutions that work, but solutions that serve actual business goals while operating within budgetary parameters.
What Skills Are Needed to Become a Data Engineer?
A data engineer must have strong analytical skills in order to draw insights from large amounts of data. Software engineering skills are required for building pipelines, extracting meaning from data, and visualizing results in an interactive manner.
A data engineer should be skilled in one or more types of Structured Query Language (SQL), such as PostgreSQL or MySQL. Further, it’s useful for them to also know coding languages like Python, Scala, Golang, C/C#, R, and Ruby. They also need to have a good understanding of ETL and reverse ETL best practices, as well as big data technologies like Hadoop and Kafka.
As with many positions within data analytics, your day-to-day responsibilities will vary depending on your area of expertise and which stage of data analytics you’re at. If you’re just starting out as a newbie data engineer, you’ll need to spend time getting to know industry-specific tools and processes, as well as learning how your organization specifically handles data analytics.
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