According to LinkedIn, data engineering jobs are on the rise at around 50% YoY and for good reason. As companies rely on data more than ever before, they need data engineers to manage and process it effectively. This trend is expected to continue as more devices and applications become connected and generate more data.
Despite the growth of the field, businesses face significant challenges when it comes to recruiting top-notch data engineers. There is a shortage of data engineers with the right combination of technical and soft skills. With the role in high demand, businesses often compete with other companies to hire the best candidates.
Using some of the top sites to hire can eliminate these problems for you. For instance, when you work with Toptal, everyone has been vetted before you ever log on. There is no work for you to do to find the next member of your team.
Working with freelancers and remote workers offers a lot of perks. A freelance data engineer can be more cost-effective than hiring a full-time employee, especially for short-term projects. It's also easy to find and hire a data engineer who has a very special skill set.
What Does a Data Engineer Do?
What does a data engineer do, and why is it even necessary to hire data engineers? It really depends on where your business is when it comes to growth. Once you have several data scientists and analysts generating information, it's time to bring in a data engineer to support them. Data engineers can take care of many things, including:
- Designing and implementing a robust data architecture that can support your business's data needs, including building data pipelines, data warehouses, and data lakes that can store and process data effectively
- Integrating data from various sources, including databases, APIs and third-party services, into a single system
- Building efficient ETL (extract, transform, and load) processes that can transform data from raw format to a format that is easy to work with
- Ensuring that the data your business works with is of high quality by implementing data validation and cleansing processes
- Designing a scalable data infrastructure that can grow with your business's needs
- Implementing analytics and reporting tools that allow businesses to make data-driven decisions
Each of these processes will enable your business to operate more efficiently and put you in a better position for growth in the future.
|What is data engineering used for?||With today’s technology, the amount of available data increases exponentially every day. Businesses can harness the power of that data to glean vital information such as customer behaviors and predictive analytics. This knowledge enables informed decision making and operational improvements. Data engineers build scalable, secure architectures to store, integrate, and process data.|
|Advantages:||1. Data engineers can automate data-related duties, releasing employees to perform other tasks. 2. Efficient data analysis, enabled by data engineers, lets business owners make better decisions. 3. Data engineers provide facts gathered through data analysis to help companies relate better with customers through marketing and interactions. 4. Data engineers improve data quality, which can reduce costs and increase operational efficiency.|
|Disadvantages:||1. The exorbitant rate of data may lead to poor system and server performance. 2. Older technologies and data overloads may hinder your access to vital data. 3. Without a thorough and effective data governance plan, data isn’t always accurate; bad data is often costly.|
|Related programming languages:||Python, SQL, Java, PostgreSQL, R language|
|Freelance average hourly rate:||$62 per hour|
|Where to hire?||Toptal, Upwork, Freelancer, and the other top sites in this guide.|
Hiring a Freelance Data Engineer
If you need to add one or more data engineers to your team, you must decide if you’ll use part-time, full-time, or freelance engineers. Each has advantages, but freelancers are often your best bet. Why?
- You won’t have additional expenses such as overhead costs, payroll taxes, and benefits.
- Some skill sets are rare and difficult to source from local talent pools. With a broader search area, such as Toptal’s global platform, there’s a much greater chance of finding engineers with those skills quickly.
- When you utilize freelancers, you agree to by-the-hour or other project-based pricing and hiring structures. When the project is finished, so is your contract with the freelancer. Hiring employees is a more permanent, committed step; if you terminate them when your project is completed, you’ll likely pay unemployment and a severance package.
- Many freelance sites offer risk-free trial periods that allow you to assess an engineer’s performance and fit.
- You can often hire qualified freelancers in days, as opposed to weeks or months with employees.
Hiring freelancers provides cost savings and invaluable flexibility.
|Freelance platform||Trustpilot Score||Key Point||Vetting||Guarantee||Free to post projects||alent status||Rate & Share|
|1||Toptal||4.8||High-profile clients||Top 3% Talent. Rigorous Vetting||Pay only if satisfied||$500 as a credit for the first project||Freelancers||Yes|
|2||Upwork||3.9||Large freelancer marketplace||Additional Expert-Vetted program||Payment protection (escrow)||None, client pays commission||Freelancers||Yes|
|3||Freelancer||4.4||Employer control throughout||With the Preferred Freelancer Program||Milestone Payment||None||Freelancers||Yes|
What Skills Should You Look For in a Data Engineer?
To hire data engineers that will help you succeed, you need to know what skills to look for. These professionals need both technical qualifications and soft skills to succeed.
Here are the top technical attributes:
- Data modeling to support complex data processing and analytics
- Managing databases, including database design, installation, configuration, and maintenance
- ETL (Extract, Transform, Load) processes that can transform and load data from multiple sources
- Familiarity with big data technologies, such as Hadoop, Spark and Kafka, to manage large volumes of data
- Experience in cloud-based data technologies, such as Amazon Web Services, Microsoft Azure and Google Cloud Platform
- Programming languages: Data engineers should have expertise in one or more programming languages such as Python, Java, R, and SQL
- In addition to these programming languages, data engineers may also have experience with other languages and tools such as Scala, NoSQL databases and Apache Spark.
Soft skills are critical as well and should include:
- Excellent communication skills to collaborate with other teams and stakeholders
- Strong problem-solving skills to identify and solve complex data-related problems
- Analytical thinking skills to analyze data and identify trends and insights
Your ideal candidate should be able to use all of these attributes together to support the goals of your company.
How Much Does It Cost To Hire a Data Engineer?
Part-time or full-time data engineer employees earn an average of $128,704 annually or $62 per hour. Yearly earnings range from $49,500 to $178,500; an engineer’s skill sets, years of experience, and location are determining factors.
Tips for Writing a Data Engineer Job Description
Your first step in the hiring process is to define your project parameters:
- Desired start time and project duration
- Detailed project description, specifying its nature and scope
- The number and types of hires you’ll make
You need to describe your position in an appealing way. Highlight your company’s successes and culture, along with how you envision the data project helping your company. Include role-specific details such as duties, qualifications, and requirements.
Before you begin interviewing applicants, compose a standard set of questions and create a consistent scoring model. That way, your results are inclusive and unbiased.
Common Interview Questions To Ask When You Are Looking To Hire a Data Engineer
Applicants who make it to your interviewing step have technical skills that qualify them as top candidates. Data engineers have more responsibilities than most, so you should require each one to complete identical technical projects to:
- Learn how they approach and resolve problems
- Gauge how well they work under pressure
- See how quickly they complete the project
- Identify the steps they take to ensure accuracy
Having impressive qualifications got them to the interview, but seeing each candidate in action and comparing your observations is priceless.
Interviews are your chance to gauge soft skills. Develop a situational scenario, but omit some pertinent details. A good engineer will ask for additional information before answering the question. You’ll gain invaluable insights into the candidate’s thought processes and communication skills.
Other interview questions should include:
- How did you contribute to past projects and what did it achieve? You’ll obtain data about problem solving, competence, quantifiable results, and the engineer’s level of confidence.
- Do you work well with teams? or How would you manage a team? Depending on the role, you’ll learn about work habits, collaboration abilities, and leadership skills.
It’s often difficult to decide between top candidates. When you hire a freelance data engineer through sites like Toptal, you have the opportunity to see if you made the right choice during a risk-free trial period.
Why You Need To Find and Hire Data Engineers
When you take the time to find data engineers for your team, you're investing in a better way of doing things at your company. They can help you put your data to better use so your company makes smarter decisions.
To get the right talent, embrace high-quality freelance sites that screen data scientists and deliver the best to you. From Toptal's curated matches to Gun.io's flexible practices, you can find a site that fits your budget and timeframe while delivering a data engineer who will do a great job.
Use the information and top hiring sites we provide in this guide to source and hire the best data engineers for your project, then prepare yourself to count the operational improvements you’ll realize.
Hiring Data Engineers - FAQs
- Are Data Engineers In Demand?
New uses for datasets emerge almost daily, and these uses depend on a solid database infrastructure. The role of a data engineer is one of the most in-demand jobs, with an expected occupational growth rate of 36% by 2031.
- Where can I find Data Engineers for hire?
Explore more sites in our comprehensive list of top companies.
- How do you hire a data engineer?
You’ll likely want to find a data engineer whose completed projects are similar to yours. Before you start looking, determine your project’s budget, goals, timeline and exact nature. You may prefer to search for engineers who are familiar with your industry and have:
- Experience with big data and cloud-based technologies
- Extensive database, data analytics and data processing knowledge
- Analytical thinking and problem-solving skills
- Expertise in specific programming languages
- Project management skills
Use top sites to find one or more part-time, full-time or freelance data engineers. You can post your positions, browse engineer profiles and portfolios or utilize talent-matching services.
- How Much Does It Cost To Hire a Data Engineer?
The U.S. national average salary for data engineers is $128,704 per year or $62 per hour. Annual salaries are between $49,500 and $178,500 depending on location, years of experience and skill levels. Hiring freelancers eliminates the need to pay overhead or related payroll expenses.
- Why should you hire a data engineer?
The world’s reliance on data is increasing as more applications and devices utilize databases. Database engineers have many responsibilities, such as:
- Assessing your database requirements
- Developing, implementing and maintaining a database architecture (data pipelines, lakes and warehouses) that meets your needs
- Gathering and processing large quantities of raw data
- Ensuring that your data is secure and high-quality
- Implementing tools to assist with data-driven decision making
Hiring a data engineer to streamline your data processes can give you a competitive edge and save resources, time and money.