Data engineers are responsible for building and maintaining an organization's entire data ecosystem. Learn more about the job responsibilities of a data engineer in this guide.
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Data engineers are responsible for building and maintaining an organization’s entire data ecosystem, which includes everything from data sources and databases to data storage solutions.
The roles and responsibilities of a data engineer include building data pipelines that are used to transport data from a data source to a data warehouse. These pipelines are crucial: they are what enable an organization to access and analyze its data, and use the insights to make business decisions. Data pipelines transport and transform data according to established business rules or a line of exploratory analysis the business wants to undertake.
Many of the key responsibilities of data engineers involve working side by side with data scientists to build custom data pipelines to facilitate data analysis projects. These responsibilities include but are not limited to the following eight key responsibilities.
Raw data describes data in its most basic, unstructured digital format. Unstructured data may consist of text, images, sound, and video, such as emails, PDFs, or voice transcripts. This data does not conform to an existing data model and needs to be converted into a form that is efficient for automated processing and analysis. Data engineers develop machine learning models of classification or clustering to scan, label, and categorize unstructured data by being trained to recognize key data points through entity extraction (extracting names, location, organization, and so on), geotagging, and classification (predicting what a piece of text is about and categorizing accordingly). This process gives rise to structured data: objective facts and numbers that most analytics software can collect and interpret. Structured data can also be easily stored and organized in Excel, Google Sheets, and SQL, and lends itself to standard data analysis methods like pivot tables or regression analysis. Converting unstructured data into a usable format has become easier and cheaper than ever as with advances in machine learning and greater access to major computing power.
Data pipelines refer to the design of systems for processing and storing data. These systems capture, cleanse, transform and route data to destination systems, taking raw data from a SaaS platform such as a CRM system or email marketing tool and storing it in a data warehouse so it can be analyzed using analytics and business intelligence tools. Data scientists and data analysts rely on data engineers to build data pipelines that enable the organization to collect data points from millions of users and process the results in near real-time. They also need pipelines to perform exploratory analyses to answer business problems such as why customers churn or how to improve sales of a lagging product. A data pipeline consists of the following:
Developers can build their own pipelines or they can use a SaaS data pipeline instead, which, while still customizable, is more of a plug-and-play solution.
To make raw data useful to the organization, data engineers must understand business objectives. A standard data engineering responsibility is building algorithms and pipelines that make it easier for anyone in an organization to access raw data. For this reason, a data engineer’s job scope includes understanding business requirements and where data fits into the business model so they can build a data ecosystem that serves the organization’s needs.
Depending on the size of the organization, a data engineer’s job responsibilities may also perform some of the functions of a data scientist or data analyst, which includes performing complex data analysis to find trends and patterns and reporting on the results in the form of dashboards, reports and data visualizations. In a large organization, data engineers will work alongside a data scientist or data analyst to provide the IT infrastructure for data projects.
Before they can create data models, data engineers must ensure the data is complete (no missing values), has been cleansed, and that rules have been established for outliers (eliminate, ignore, average out, and so on). Predictive modeling is used to determine future events based on historical data, while prescriptive modeling goes a step further, using current and historical data to recommend a strategy or course of action.
Data pipelines represent an automated set of actions that extract data from various sources for analysis and visualization. These processes are powered by algorithms. For example, “take these columns from this database, merge it with these columns from this API, substitute outliers with the median, and load the data in this other database.” This is known as a “job” and pipelines are made up of many jobs.
Some companies hire data engineers to build bespoke analytics software in-house for greater customization and data accuracy. The most common programming languages used for this are C++, Java, and Scala. Other data engineers use SaaS analytics tools to manipulate and analyze data, or they might be asked to build an analytics stack. The stack would consist of data collection tools like Segment and mParticle, which collect data from a website or app and route it into a SQL data storage system, plus a data visualization tool such as Tableau or D3.js.
Part of a data engineer’s job duties is to provide the IT infrastructure for data analytics projects. In large enterprises, they work side-by-side with data scientists to create custom data pipelines for data science projects.
Ready to switch careers to data engineering?
Data engineering is currently one of tech’s fastest-growing sectors. Data engineers enjoy high job satisfaction, varied creative challenges, and a chance to work with ever-evolving technologies. Springboard now offers a comprehensive data engineering bootcamp.
You’ll work with a one-on-one mentor to learn key aspects of data engineering, including designing, building, and maintaining scalable data pipelines, working with the ETL framework, and learning key data engineering tools like MapReduce, Apache Hadoop, and Spark. You’ll also complete two capstone projects focused on real-world data engineering problems that you can showcase in job interviews.
Check out Springboard's Data Engineering Career Track to see if you qualify.
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