How to hire outstanding data engineers: the definitive guide
For Employers

February 15, 2024

How to hire outstanding data engineers: the definitive guide

Big data is changing the way we do business, creating a need for top data professionals. So, along with data scientists who help us manage those large quantities of information and create algorithms, there are data engineers, the architects of data platforms.

With over 2.5 quintillion bytes of data generated every day, Data Engineers are busier than ever. The more information we collect, the more we can do with it. 

At its core, processing data requires an ecosystem - Data Pipeline, which is more like a dedicated environment where data is obtained, stored and processed. This data can be further applied in machine learning, business intelligence, stream analysis, and many more. 

In this article, we’ll explain how to find the next Data Engineer superstar for your business. We’ll go from the big picture to the details.

How to find, attract & hire top data engineers in a hot job market

Finding the right engineering talent is crucial for the growth of your company.

According to LinkedIn's Emerging Job Report, the hiring growth rate of Data Engineers has increased by 35%. The Data Engineering role is ranked 8th which makes it one of the most booming jobs. This means that now it’s harder than ever for companies to hire and retain talents in this flourishing industry. 

Here are a few tactics you might use to streamline your hiring process for a top engineering expert.

  1. Getting started

The hiring process starts with generating a talent pipeline of the right candidates ready to fill the open position. 

According to Sergiu Matei, hiring an engineer is not the same as selling a product or service. As Adam DuVander explains in his book Developer Marketing Does Not Exist, resonating with engineering talents requires more education and less promotion. To pique their interest, publish valuable content, such as posts about upskilling, industry trends or exclusive data insights and do not overly mention your brand. Host events like webinars, hackathons and round tables to showcase your team and culture. Moreover, focus on building a great relationship with the data engineering community. This will help you stay relevant and position yourself as a great place to work where great ideas and candidates are created and fostered.

The key to hiring brilliant data engineers is to share your story and help them understand whether they could fit in it. You’re not just inviting them to fill your position. You’re selling them the next chapter of their career.

  1. Create a first-class interview process 

On average, a Data Engineer specialist will find a job in 20 days. The best performers will stay on the market no more than ten. 

The longer you wait, the better quality candidates you miss out on. Our advice is to take that opportunity as quickly as possible. Like a resume? Email the prospect. Like the prospect? Interview them within the next 24 hours.

Don’t let your talents jump through those excessive interview hoops as they may cause a lack of interest. If you’re looking to recruit a Data Engineer professional, don’t lose the momentum and assess their fit as quickly as possible. 

Interviewing is always about creating an environment where candidates can perform at their best:

  • Keep them engaged during the interview
  • Create a sense of your company’s culture, approach, and values
  • Make a great first impression as an employer.

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  1. Offer them a clearly defined position

People like to know what to expect from the new job. They want to know how big the team is, what needs to be done, what team they will be working with, what projects they will handle, what impact you expect them to have. 

When looking for a new role, data specialists crawl for a detailed and realistic job description. 72% of Data Engineers testified that job description was the key factor in their job application process. And if it's done wrong, they will pass up and look for another opportunity somewhere else.

  1. Provide the right technology stack

First and foremost, Data Engineering is the new Business Intelligence and Data Warehousing. Second, just like Data Scientists, Data Engineering Experts do the coding and are interested in analytics and data visualization. But the main difference, Data Engineers are more inspired by Software Engineering.

Just because technology is everything for a Data Engineering Expert, it is no surprise why 48% of them stated that the tech stack they will work with is the most decisive factor in accepting the new role. 

Below you will find the technologies used by Data Engineers. This list can include, but not limited to:

  • Batch: Hadoop, Hive, Apache Spark
  • Streams: Apache Kafka
  • Infrastructure: Cloudera, Hortonworks, Mapr
  • Automation: Ansible, Chef, Jenkins, Airflow, Luigi
  • Cloud: AWS
  • Language: Scala, Java, Python
  • Database: SQL, NoSQL, Datamarts
  • Visualization: Kibana, Grafana

Nice to haves:

  • R for Basic grasp of statistical tools
  • Software engineering: Java, C, C#, PHP, Javascript - for app development projects

For you as an employer it’s important to paint a picture of your project requirements, so that Data Engineers can understand if they are a good fit for them. 

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  1. Offer a competitive salary

According to the research, 42% of Data Engineers say they are most likely to decline a job offer because their salary and benefits are below the market rate. Therefore, it’s essential to make sure your salaries and compensation packages are competitive enough to attract and retain in-demand software talent. 

According to Payscale, the average yearly salary in the US for a Data Engineer is $93,147. At the same time, the average Amazon.com Data Engineer earns $130,526, while the typical Google Data Engineer compensation is $120,639. For Google senior data engineering positions salaries can even overshoot $200k.

Fighting established companies for top data programmers is difficult. They are resume boosters, offer bigger paychecks and more resources. After all, they don’t win every battle. Growing businesses have what it takes to compete with them. Here’s how they can pull attention away from the Goliaths.

Whether paychecks are part of the job offer, there is more you can use to go beyond that. Highlight what makes your company unique and exciting. Be authentic and invest in your team’s performance and personal development to show your presence and build a community. Always write about interesting problems you solve and share what the team does.

Don’t sell a dream and deliver a nightmare, and you just might attract a data engineering team worth big brands’ envy. 

  1. Recruit in the right places and with the right people

According to the predictions, software engineering roles will increase by 22% in 2030. With the need for nearly a quarter more software programmers, companies have to think outside the box and scale their search beyond the borders.

People who are heavily in the Data and Tech worlds use LinkedIn, Glassdoor, or their networks to have their resumes spotted. On the other side, talents who live in emerging markets use LinkedIn less frequently, even though these locations harbour some of the world’s most promising Data Engineers. 

This means that the best tech talents that can solve the biggest data challenges are even harder to find and recruit. 

Flexibility is key in this situation. Understand what makes a good Data programmer and what requirements do you have. Do the advertising on your company website, LinkedIn and Glassdoor

  1. Outsource your efforts

Since the demand for developers and data engineers outweighs the local talent pool, the hiring process climate is fiercely competitive.

A study conducted by Dr. Andrew Chamberlain of Glassdoor found that recruiters spend 35 days on average sourcing, conducting interviews and negotiating offers for a software engineer with at least 30 interview reviews.  

One of the most effective ways to hunt for talented Data Engineers is to make use of a hiring platform. By partnering with one of them, you will not only be able to ease your talent crunch, but also gain access to a huge network of highly-qualified Data Engineers. You will either be able to build a highly-driven remote offshore data science team by diving into the emerging and untapped workforce markets, like Central Eastern Europe, Middle Asia, South America and Africa. Home to millions of excellent engineers, these markets have become the world’s best alternative to Silicon Valley-caliber talents.

The other benefits may include:

  • Fast hiring
    Talent networks allow you to save time recruiting through a comprehensive sourcing and automatic matching with best-fit options for the job. Their candidates are middle or senior level professionals from diverse talent pools. This means, you don’t have to create a job ad, post it and wait for applicants to trickle in. You can go directly to your focused talent network and source from there. 
  • Intelligent vetting
    Hiring platforms do a vetting and screening process to evaluate a candidate’s qualifications and background and eliminate unqualified prospects from the pool of applicants. It can include background checks, screening interviews, technical tests, availability, culture fit, English proficiency and many more.
  • Cost reduction
    With a talent network, you can cut business costs that would otherwise go toward advertising and recruitment marketing. By hiring remote engineers, you will also cut down the time-to-hire, office, utilities, cleaning, furniture expenses.
  • Scalability
    Talent networks take the heavy lifting. They allow your company to scale up quickly by not investing a lot of time or resources in talent acquisition and interrupting your daily operations.

The world's emerging markets are home to many high-qualified and experienced data engineers that can bring value to your company. Treat them as a part of your team and they will always perform at their best.

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  1. Close the hire speedily

Most employers are looking for a “perfect match” who can tick off all the checkboxes in their job requirements list, but the truth is that there’s no 100% fit. This ends up narrowing down the candidate pool you can find and hire, spending a lot of precious time.

Once you've found your all-star engineering superhero, hurry up and close the hire as soon as possible. Keep continuous communication with the candidate after the recruitment. Be responsive and remind them of your company story, values, and why they should be excited to be joining you.

 

Skills & Qualifications to Look For In a Top Data Engineer

Knowing what makes a great data engineer is a critical first step towards identifying and onboarding the right candidates. Here is a list of the most crucial skills you should look for when hiring your next Data Engineering power player.

Data Roles and Skills set information in circles

Coding

Brilliant Data Engineers are proficient programmers. In order to perform their responsibilities on a high note, they should have a strong grasp in Java, Golang, Python, Scala, RLang, C, C# & C++.

Most tools and systems for big data are written in Java (Hadoop, Apache Hive) and Scala (Apache Kafka, Apache Spark). Python along with Rlang are widely used in data projects due to their syntax and popularity. While C/C# and Golang languages are often used for implementing Machine Learning models. 

Collecting Data

Proficient Data Engineers make the complex process of collecting data as smooth and effective as possible. Their main responsibility is to handle the volume, velocity and variety of massive data sets from new and unstructured sources. 

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Transforming Data

With the company growth, transforming data is a must-do. This allows you to keep up with new technologies and market demands. A fast-performing Data Engineer will help you collect data and offer concrete strategies to make the most of it.

Data Warehousing 

How will a high-qualified Data Engineer capture, select, inspect, analyze, organize and store data is what will make the difference for your company. This is why, experience with frameworks and libraries like Hive for data warehouse and knowledge of both SQL & noSQL for database management management are a must for a Data Engineering Expert, helping companies make better and more informed decisions. 

Data Analysis 

Data engineering is not only about collecting, transforming and storing information. Data engineers should have a strong grasp of data modelling and algorithms and a clear understanding of analytics software - MapReduce, Hive, Pig and HBase - time-saving resources for efficient data analysis. 

Data Expertise

Data Engineers are in charge of building ETL (data extraction, transformation and loading), storages and analytical tools. So experience with ETL and BI solutions is a must.

Project Management & Communication Skills 

Data Engineers are part of the Data Science Team, along with Data Scientists and Data Architects. They also work with people without a technical background for different projects. This is why possessing good project management and communication skills are so demanding. It allows them to be a team player, collaborate better and share insights, findings and suggestions more professionally.

 

Why Hiring Platforms Are the Best Way to Hire Senior Data Engineers? 

Top Data Engineers are difficult to find. Many of the brightest talents have already been hired to work at big companies, creating a vacuum of talent in the marketplace. But that doesn’t mean that talent is unavailable.

So, what’s the solution? Is it possible to hire cost-effective data engineering professionals quickly from a limited pool of talent?

The answer is yes. With Index, companies can now hire highly talented remote data engineers from across the globe in just a few days and for a fraction of the cost, through: 

Smooth hiring process

Index handles your hiring challenges by matching your company with senior-level Data Engineers. Our innovative recruitment method focuses on interviewing and testing top-calibre remote candidates with excellent qualifications, verified skills, good English proficiency, available in your time zone. 

Diverse source of talents

Index is a global network of exceptional Data Engineers. We tap into local markets with lower salary indicators without affecting the quality. Your company will be able to reduce the hiring budget by offering a well-paid Data Engineering job above their local average. 

Highly-skilled data engineers

We test their expertise in many areas like databases, machine learning, data warehousing, Python/Java proficiency, Hadoop, cloud computing, etc., and only then select the best candidates. Hence, when you decide to hire with us, you’ll be able to source the best possible talent matched with your industry, company culture, and project type.

 Let's partner. Talk to an Index.dev representative today ->

To find out more about how we can help, hit us up and tell us about your project so that we can get the work started.

If you’re a Data Engineering Expert looking to work for the world’s leading companies, click here to join our inspiring community of software engineers from all over the world. 

Ready to improve the hiring game of other software talents? Read our previous post to discover more ways and tactics you might use to mitigate the time and efforts your HR team spends on hiring in 2022.

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