Both data engineers and data scientists play a key role in growing your operations. Learn about their roles and the tools and technologies they use in their workflows.

Companies are amassing more data than ever before. This helps them identify emerging trends and gauge performance, enabling more informed decisions.

However, data on its own provides little value. It needs to be cleaned and modeled to provide companies with the necessary insights.

There are three important roles companies hire to help with their data engineering and data science needs: data analysts, data engineers and data scientists. However, data engineers and data scientists have the heaviest engineering chops and some overlap in skills.

So, what’s the difference between data engineers and data scientists — and what are their roles? What tools and technologies do they use in their jobs?

This article will cover the differences between data engineers and data scientists. It’ll also look at how each supports your data initiatives and which of the positions you should hire.

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What is a data engineer?

A data engineer works behind the scenes to create the database infrastructure that your company will use to store and access data. You can think of them as “data architects.” They aim to make data easily accessible to the stakeholders within an organization.

Data engineers typically have educational backgrounds in computer science and software engineering. They earn an estimated $99,242 per year and are listed as the eighth fastest-growing job in the United States.

As companies become more data-oriented, they need to build a solid foundation on which to collect, analyze and model data. Whether you’re a startup or a growing business, working with a data engineer can prove valuable.

Responsibilities and core tasks

A data engineer’s primary role is to build and maintain a company’s data infrastructure — the systems that enable data consumption, storage and sharing. This involves building data pipelines that ingest data from external sources and move it to a repository, like a data warehouse or data lake.

However, much of the data from these sources is still in its original state, meaning that it hasn’t been processed or organized yet. As a result, the data might not be validated or might contain errors. Data engineers use various programming languages like SQL and Python and processes like Extract, Transform, Load (ETL) to prepare this data for analysis.

ETL consists of three stages:

• Extract: Raw data is pulled from sources like customer relationship management (CRM) platforms, IoT devices and clickstream logs. The data is then put into a staging area where it can be validated before it goes into a data repository.
• Transform: The data must be properly structured and clean before it can be used for analytics. Data engineers will create and apply formatting rules to data sets to remove redundant and unusable data.
• Load: Data engineers will then set up a cloud-based data warehouse, like Snowflake or Redshift and set up processes to move the data from the staging area.
  

In short, data engineers ensure that a company’s architecture will support the requirements of the company they work for.

Languages

Data engineers use a variety of languages to build data architectures, develop modeling processes and create reliable data flows. These include:

• Python: A high-level programming language that helps data engineers build efficient data pipelines, write ETL scripts and perform data analysis.
• Java: An object-oriented programming language that’s used to develop desktop and mobile applications. Data engineers use this language to build data systems.
• Scala: A general-purpose language that data engineers use to structure, transform and serve data.

Orchestration and workflow management

Data orchestration is at the center of data infrastructure. It involves collecting data from disparate sources and organizing it into one cohesive system. Data engineers use the following data orchestration tools:

• Airflow: An open-source data orchestration tool used to schedule, orchestrate and monitor batch-oriented workflows. Airbnb originally developed it.
• Luigi: A python package that creates more advanced pipelines to accomplish batch jobs. Spotify originally developed it.

Big data frameworks

Big data allows organizations to gather meaningful insights from their data and make informed business decisions based on those insights. Data engineers use their engineering skills with the following big data frameworks to process large amounts of data:

• Hadoop: An open-source framework capable of processing large data sets across clusters of computers using simple programming models.
• Spark: An open-source analytics engine built for large-scale data processing. Like Hadoop, it splits up workloads across different nodes.

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What is a data scientist?

A data scientist relies on the data infrastructure that a data engineer builds. They use different types of analytics and reporting tools to identify and extract meaningful insights from large amounts of data.

Data scientists also create predictive models to produce forecasts based on historical data, as well as prototype data-driven, automated systems. Their work enables companies to leverage machine learning (ML) and artificial intelligence (AI).

In addition to mining big data for insights, data scientists present their findings to non-technical stakeholders. This means they must be proficient with data visualization tools and have strong communication skills.

Demand for data scientists is projected to grow 36 percent from 2021 to 2023 — faster than average compared to other jobs. Because of their specialized skill set, data scientists earn more on average than data engineers — an estimated $124,112 per year.

Responsibilities and core tasks

Companies bring on data scientists to put their data to work and ensure they’re not just collecting data for the sake of it. Their responsibilities include taking data sets prepared by data engineers and creating predictive learning models.

Predictive learning models rely on sophisticated algorithms to predict future behaviors based on historical data. They can also be trained to ingest and respond to new data over time. For example, a company might use predictive models to estimate the chances of a lead converting.

When building these models, data scientists leverage their skills to prototype ML/AI systems. Their goal is to identify trends and communicate their findings to business stakeholders.

Languages

Data scientists use many languages to build data models, create ML/AI systems and solve complex problems. These include:

• Python: A high-level programming language that offers data science libraries like NumPy, Scikit-learn and Matplotlib to build data models, create visualizations and more.
• Java: An object-oriented programming language used to create statistical models, train machine learning algorithms and more.
• R: An open-source programming language that data scientists use to create statistical models and data visualizations.
• Scala: A general-purpose language that supports object-oriented and functional programming. It’s designed to take advantage of big data processing.

Big data frameworks

Like data engineers, data scientists rely on big data frameworks when they work with large data sets. These frameworks include

• Hadoop: An open-source software framework that enables data scientists to perform data operations on large data sets across clusters of computers.
• Spark: A data processing engine capable of performing batch processing and stream processing.

Computing platforms

Computing platforms include a suite of technologies and tools for data management, advanced analytics and machine learning. Data scientists use the following tools:

• Jupyter: A web-based development environment that lets data scientists build and arrange workflows for machine learning projects.
• R notebook: An R Markdown document that data scientists use to orchestrate an end-to-end data analysis workflow.

BI platforms

Business intelligence (BI) turns raw data into actionable insights. Data scientists use the following BI platforms to perform advanced statistics and predictive analytics on their data sets:

• Tableau: A modern BI platform that helps data scientists visualize large quantities of data and create more accurate forecasts.
• Looker: A big data analytics platform from Google Cloud that data scientists use to set up real-time dashboards, create data-driven workflows and more.
• Power BI: A BI tool from Microsoft that offers top-notch data visualization and built-in AI capabilities.

How do data engineers and data scientists support your company?

Companies are amassing more data now than ever. Enterprises are expected to see a 42.2 percent annual growth rate in the amount of data they collect from 2020 to 2022.

Despite the amount of data companies are collecting, as much as 68 percent of it goes unleveraged. This means companies are not getting value from most of the data they collect. Data engineers and data scientists can help you unlock your data’s value.

For example, let’s say that you operate an e-commerce store and you have data across various sources, including sales data from your online store, customer data from your CRM and marketing data from your advertising platform.

A data engineer can build a data pipeline to pull in data from these sources and set up a repository to store it. Once a data infrastructure is in place, a data scientist can run statistical analyses to uncover insights. They can also construct a predictive model for customer preferences based on purchase history, enabling your site to recommend products.

Data scientist vs. data engineer: Which should you hire?

Data engineers are a must-hire for any growing organization. They build the necessary data infrastructures your company will need to store, process and analyze data.

Data scientists also play a critical role. Once data has been prepared and modeled, they can feed it into analytics programs and create predictive models to inform decision-making.

But how do you know if your company is ready to hire data scientists?

While data scientists are certainly invaluable, you need to ensure that your company is ready before bringing them on board. Otherwise, they’ll just sit around doing basic data analysis.

Let’s look at the data science hierarchy of needs:

Data science is potentially the highest-value use of data, but it depends on a solid data engineering foundation. It entails:

• Setting up a modern data stack
• Establishing data governance standards
• Creating a data-driven culture
• Building a robust data architecture

Once you have each “layer” in place, you can consider working with a data scientist to help you create predictive learning models and prototype ML/AI systems.

How Fivetran helps your data engineers

Ninety-eight percent of data engineers face challenges when building new pipelines. However, a lack of strategy and unreliable pipelines prevent many companies from extracting the full value of their data.

Here’s how the data integration platform created by Fivetran can help you build a solid data engineering foundation.

Connect to your databases and apps

The number of data sources is seemingly endless. Building custom data connectors for each one only bogs down your data engineers and keeps them from doing more productive work.

Our fully managed data connectors let you connect to a broad range of data sources with minimal configuration, drastically reducing the time it takes for your engineers to build data pipelines.

Transform your data

Data is a valuable asset, but it must be properly organized and structured. Fivetran Transformations enable your data teams to orchestrate SQL-based transformations. They won’t have to change environments because the Transformations are accessible from the GUI.

Check out our Transformations setup guide for more details.

Replicate your data

Running analytical queries and machine learning algorithms is resource-intensive. Replicating your data to a high-performance cloud environment will reduce the burden on operational databases and support your business intelligence and machine learning needs.

Our High-Volume Replication (HVR) Solution supports real-time data movement to your data sources. Only data changes are moved to reduce the load on your operational systems.

Build a strong data engineering foundation with Fivetran

Data engineers and data scientists are valuable assets for any company — data engineers build your data infrastructure and data scientists use that infrastructure to create predictive models. However, before hiring experienced data scientists, ensure that you have a solid data engineering foundation in place.

Start a 14-day free trial of Fivetran today and give your data engineering teams the tools they need to create a robust data infrastructure.

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