According to Statista, the amount of data that has been created and replicated reached a new high in 2020. The same study revealed that global data creation is expected to grow to more than 180 zettabytes by 2025. In other words, your company and industry have more access to data than ever before.
That's why you should consider hiring data science professionals to analyze this data. Most companies hire a data scientist to derive actionable business insights, but a data scientist alone may not be able to give you the insights you need. Before data scientists can analyze a dataset, they need a data engineer to build a strong data infrastructure.
Read our comprehensive guide to learn more about data engineers and how to hire one. We'll cover how much data engineers make, their responsibilities, and the skills you should look for in data engineers. We'll also cover how to find and hire leading-edge data engineering talent.
What Is a Data Engineer?
Data engineers are information technology (IT) professionals who prepare data for operational and analytical uses. They work with data analysts and scientists to gather, manage, and convert data into useful business insights.
This role requires a varied skill set, including:
- Multiple programming languages
- SQL and NoSQL database design
- Data warehousing
- Business intelligence (BI) platforms
- Unix operating systems (OS)
How Much Do Data Engineers Make?
A data engineer's skills and salary are correlated. The more skills and experience they have, the higher their salaries will be.
Junior/Entry-Level Data Engineers' Salary and Skills
Junior or entry-level data engineers have under three years of experience. Most are fresh grads with bachelor's degrees in mathematics, engineering, or computer science, but some are self-taught or graduates of boot camps.
Since they have little real-world experience and fewer skills, these engineers have lower salaries. According to Glassdoor, the average U.S.-based junior data engineer makes $70,357 per year.
Senior Data Engineers' Salary and Skills
Senior data engineers have over three years of professional experience. Because of their skills and experience, they have much higher salaries than junior and entry-level data engineers. According to Glassdoor, the average U.S.-based senior data engineer makes a whopping $135,961 per year.
What Does a Data Engineer Do?
As previously mentioned, data engineers build systems and pipelines that aggregate, manage, and convert raw data into usable information for business analysts and data scientists. Their main goal is to make data accessible so that companies can use it to optimize their choices and performance.
Additional duties include:
Optimizing Your Company's Data Ecosystem
One of the main goals of data engineering is optimizing data ecosystems. Every data ecosystem includes the following:
- Infrastructure: The infrastructure is the foundation of the data ecosystem. It includes:
- Software and hardware services that capture, organize, and collect data
- Query languages like structured query language (SQL)
- Storage servers
- Hosting platforms
- Analytics: If the infrastructure is the foundation of a house, analytics tools are the front door that teams use to access the data ecosystem house. These platforms connect pieces of the infrastructure, storing the data in one centralized hub. They also offer tools for:
- Segmenting users
- Identifying ideal customer profiles
- Sending messages to users to boost conversion and engagement rates
- Applications: The roof of the data ecosystem, applications are systems and services that make the data usable. Operations, marketing, and other relevant departments can use these applications to analyze information and create superior operation, pricing, and marketing strategies.
Big Data vs. Data Engineering
One of the most important data engineering tasks is optimizing big data ecosystems. A big data ecosystem is a collection of analytics, infrastructure, and applications used to capture and analyze big data — that is, data sets that are too complex and large to be captured, analyzed, and stored by traditional data-processing software.
Big data has a number of notable characteristics:
- Volume: This refers to the amount and size of big data that your team will manage and analyze. The quantity and size of big data are typically larger than terabytes (1,000 gigabytes) and petabytes (1,000,000 gigabytes).
- Variety: This refers to the nature and type of the data. Big data comes in many different formats, including structured and unstructured data. Structured data can be organized into pre-defined formats like databases, while unstructured data is a conglomerate of varied data types that can't fit into conventional data models.
- Velocity: This is the speed at which companies store, receive, and manage data. Unlike traditional or small data, big data is often available in real-time. It's also produced more frequently and continually.
- Veracity: This characteristic refers to the reliability and truthfulness of the data. Because the veracity of big data can vary, data engineers need to work with data analysts and scientists to capture, clean, and identify reliable data.
- Value: As the most important big data characteristic, the value of big data often comes from pattern recognition and insight discovery. If performed well, these analyses can lead to improved customer relationships, more effective operations, and other quantifiable business advantages.
Responsibilities of a Data Engineer Manager
Now that you have a clearer idea of what data engineers do, let's take a look at their responsibilities.
A data engineer's role varies depending on the company they're working for. However, most typically work as part of a data analytics team alongside data analysts and scientists.
Data engineers build pipelines and provide data in usable formats to data scientists and analysts, who run data mining, machine learning, and predictive analytics algorithms and queries to derive actionable insights. They also provide usable data to business analysts, executives, and other staff members.
Besides building and refining data pipelines, data engineers may also be responsible for creating systems that:
- Clean and consolidate data
- Make data more valuable and usable by linking it to pre-existing data
- Provide relevant data available to multiple applications and endpoints
Why Hire a Data Engineer?
There are many reasons to hire a data engineer, especially if you have a lot of company data to analyze. These include:
- Solving business problems more efficiently and effectively: A skilled data engineer can build and maintain infrastructure for answering questions and improving business processes.
- Developing data pipelines and models faster: A data engineer can also collaborate with data science and business intelligence (BI) teams to create data pipelines and models for machine learning, reporting, and research.
- Reducing cybersecurity risks: As an increasing number of businesses shift online, there's been a corresponding explosion of digital threats like ransomware and data breaches. The right data engineer for your team can encrypt and hash your data to reduce potential risks and security problems.
- Encouraging team members to adopt a data-centric culture: Last but not least, a data engineer can help you change your company culture. The best hire for your startup can educate and encourage team members to treat data as the primary way to get business insights and make decisions. They can also teach colleagues how to use, access, and analyze data.
What To Look For In a Data Engineer
Clearly, hiring a good data engineer offers many benefits. They can help you develop and refine powerful data pipelines, lessen cybersecurity risks, and even reshape your company culture.
However, hiring highly-skilled data engineers can be an uphill battle, especially if you're not familiar with data science. Use our list of top data engineering skills to help you locate suitable picks for your team.
The best data engineer for your startup should have the following hard or technical skills:
Like data analysts and scientists, data engineers must have robust programming experience. Here are the main languages to look for:
- R: A free software environment and programming language for statistical graphics and computing, R runs on UNIX platforms, macOS, and Windows. It provides a broad range of graphical and statistical techniques, including classical statistical tests, clustering, and time-series analysis.
- Python: A general-purpose coding language that includes high-level data structures, Python can be used for numerous artificial intelligence (AI) and machine learning applications. Engineers can also use Python to write Extract, Transform, Load (ETL) scripts and create data pipelines.
- SQL and NoSQL: The ideal hire should also know SQL and NoSQL. SQL is a domain-specific language for managing data in relational database management systems, while NoSQL is a non-relational way to organize data. NoSQL databases can store and retrieve data in any format as long as the method isn't relational.
Data Warehouses and Data Lakes
After extracting information from various sources, your hire needs to store the information in a data warehouse or lake.
Data warehouses store structured data, such as information stored in relational databases. On the other hand, data lakes work with any kind of data, including streaming and unstructured data.
Your data engineer should also know how to:
- Set up a cloud-based data warehouse
- Connect different data types to data warehouses and lakes
- Optimize data warehouse and lake connections for efficiency and effectiveness
Configuring BI Platforms
Once they've stored the data in data warehouses and lakes, data engineers need to use BI platforms to establish connections between information sources, such as data lakes, data warehouses, and applications. Engineers should also have enough BI experience to help data scientists build dashboards for displaying analytics and insights.
Another skill to look for is a thorough understanding of Unix-based OS like Linux, Unix, and Solaris. These OSes offer root access and functionality that macOS and Microsoft Windows don't.
Additionally, future machine learning systems will probably be Unix-based. So if you want a future-proof hire, look for a data engineer who has at least three years of experience with Unix-based operating systems.
Hard skills are important, but the best data engineer should also have soft skills like teamwork skills, an eye for detail, and patience.
Like many other IT professionals, data engineers are expected to work with other teams and departments, including:
- Data scientists and analysts: Data engineers need to work with data scientists and analysts to build, refine, and use data pipelines, BI dashboards, AI models, and databases.
- Company leaders: Like data scientists and analysts, data engineers must regularly meet with company leaders to determine how data insights can advance business objectives.
As such, they need to know how and when to talk and listen. They also need to know how to effectively explain insights and data science concepts to colleagues with non-technical backgrounds, including management.
Eye for Detail
A good data engineer should also have an eye for detail. They should be able to:
- Sift high-quality data from low-quality data
- Ensure data integrity and quality as data moves throughout the pipeline
- Identify areas for improvement and expansion
Passion for Working on the Back-End
Last but not least, your hire should have a passion for working on back-end systems. Remember, data engineers don't build many user interfaces (UIs). Instead, they primarily work behind the scenes, which means they can't point to something and say they built it. Your hire should be okay with this and take pride in creating back-end systems that users can't see.
How to Find and Hire a Data Engineer
Once you've gathered a list of the top data engineering skills, you can start finding and hiring data engineers for your team. Here's how:
1. Choose the Right Platform To Hire Data Engineers
First, you need to pick a suitable platform for hiring data engineers.
Most startups hire through sites like LinkedIn and Indeed, which offer powerful tools to kickstart the hiring process. For instance, LinkedIn offers the following tools to source and hire data engineers:
- Career Pages: This is an employer branding tool that helps you reach the right candidates by sharing your company's story. It also lets you target the right talent and showcase your jobs.
- Recruiter: An all-in-one hiring platform that helps you find and connect with professionals, Recruiter offers up-to-date insights on over 740 million LinkedIn members, advanced search filters, and recommended matches.
However, these platforms require you to vet and test candidates manually. As such, they're not the best pick if you have limited energy and time for hiring and onboarding talent.
That's why you should consider joining talent marketplaces. Unlike traditional job sites, talent marketplaces come with pre-vetted data engineering talent. Some will also help you with complex hiring challenges, such as compliance, immigration, and payroll.
2. Write a Compelling Job Description
If you decide to hire through job sites or other options that require manual vetting and testing, you'll need to write a compelling data engineering job description to attract cutting-edge talent.
Here's a sample data engineering job ad:
Remote Senior Data Engineer — Revelo
Revelo is looking for a remote Senior Data Engineer to join our data science team. This role is open to candidates in Eastern Standard Time (EST), Mountain Standard Time (MST), Central Standard Time (CST), and Pacific Standard Time (PST).
Revelo is a talent marketplace that matches Latin America's top tech talent with startups around the world. Check out our website to learn more.
- Use Kanban software development methodologies to iteratively improve our data ecosystem
- Collaborate with our data science team to write complex algorithms that provide actionable insights into our data
- Build simple and efficient data pipelines that clean, transform, and gather data from disparate sources
- Develop AI and machine learning models that can be used to answer questions and make predictions for the company
- Identify unsafe practices in pipelines that could lead to cyberattacks
Required Skills and Qualifications:
- Three or more years of experience with SQL, R, Python, and data exploration and visualization tools
- Proven track record using data warehouses and lakes to store data
- Familiarity with the Amazon Web Services ecosystem
- Strong communication skills, including the ability to explain data science concepts to non-technical staff and stakeholders
- Comfort working in a multi-disciplinary, research-oriented team
Compensation and Benefits:
- Competitive salary of $120,000 to $130,000 per year, depending on experience
- Wellness program
- Medical and dental insurance
- 8:30 a.m. to 5:30 p.m. EST
- Monday to Friday
3. Create Engaging Interview Questions
After posting your job ad and receiving hundreds of applications, read through each resume and write down the names of candidates you want to interview.
Here are some questions you should ask to learn more about each applicant's experience, personality, and work ethic:
- What drew you to data science and engineering?
- What volume of data have you worked with before?
- What is your favorite programming language for data visualization and why?
- Which Unix-based operating systems are you familiar with?
- Tell me about a time you experienced ETL issues. How did you spot this issue and how did you fix it?
- What was the proudest moment in your career and why?
- What was the biggest mistake in your career and why?
Recruit Data Engineers With Revelo
Finding the best data engineers for your startup can be challenging, especially if you're already up to your nose in paperwork. Fortunately, Revelo can help.
Revelo is a talent marketplace that matches Latin America's leading data engineers with startups around the world. All of our talent has been rigorously pre-tested for their English proficiency, skills, and experience. We have engineers specializing in every tech stack and language, including R, Python, and SQL.
Contact us today to recruit data engineers.