The size of the big data market globally is set to grow to $103 billion by 2027 which can be attributed to the growing influence of technology in our lives.
Companies today need to be data-driven to maintain a competitive advantage. The two key roles to enable this today are Data Engineers and Data Scientists.
However, the growing need for these professionals in the organization has also created fierce competition in hiring them.
This article provides a detailed guide to hire Data Scientists and Data Engineers.
Data Scientists & Engineers: Definitions, Roles, Differences
The definitions and roles of Data Scientists and Data Engineers are different, although there may be some overlap in the tasks they do. Below we discuss each of these roles and how they differ.
What is a Data Scientist?
A Data Scientist extracts meaningful insights from raw data by conducting various analysis. Then, they recommend and develop solutions based upon these findings.
What Does a Data Scientist Do? Roles and Responsibilities
The roles and responsibilities of Data Scientist’s revolves looking at the facts and patterns of the business to help management make evidence-based decisions and solutions.
So, what does a Data Scientist do on a daily basis?
- Identifying key business challenges and prioritizing them in collaboration with management stakeholders
- Cleaning, organizing, and preparing both structured and unstructured data
- Analyzing raw data utilizing different data mining techniques to identify patterns (e.g.: Decision Trees, Regression Analysis, Clustering, etc.)
- Utilizing machine learning algorithms to create predictive models
- Communicating insights to business stakeholders using narratives and visualizations
- Proposing feasible and viable solutions to tackle the business challenges.
What Is a Data Engineer?
Today, companies have massive amounts of data that need to be made accessible before Data Scientists and business analysts can interpret them.
This is where Data Engineers help through delivering high-quality data by creating and managing the entire data infrastructure which collects, organizes, and delivers data for analysis.
What Does a Data Engineer Do? Roles and Responsibilities
The key duty of a Data Engineer is to ensure the quality and accessibility of data by building data pipelines. Their key focus spans around the three main stages that data undergoes called ETL (Extract, Transform, Load) before business users utilize it.
Some key Data Engineer roles and responsibilities include:
- Designing the architecture of a data platform
- Setting up and maintaining databases and data warehouse
- Cleaning and structuring data using tools such as Apache Spark and SQL
- Building, testing, and maintaining the database pipeline
- Conducting performance optimization for efficiency and cost-savings
- Maintaining compliance with data governance by doing data quality checks.
Data Engineer VS Data Scientist: What’s The Difference?
The difference between Data Engineer and Data Scientist is that Data Engineer’s main priority is building and maintaining the data pipelines to make data accessible.
On the other hand, Data Scientist’s prioritize finding insights from this data utilizing statistical techniques and building machine learning models.
Data Science and Data Engineering Importance
It is estimated that the world will be producing 463 exabytes per day of data by 2025. Both of these roles play a pivotal hand in maximizing the ability of organizations to manage and make use of this data.
Why Is Data Science Important?
Only when raw data is converted into insights through analysis, does the data become useful. This process provides both insight into what’s happening and foresight into what might happen.
Furthermore, Data Science holds the key to creating personalized user experiences and streamlining business processes.
A case in point, Amazon has a team of Data Scientists which has created a personalized recommendation system using machine learning that predicts products users are likely to be interested in based on their historical purchase pattern.
Why Is Data Engineering Important?
Data Engineering is the first step an organization takes to make the data useful as Engineers help in preparing and processing it for further use.
For instance, the development of data pipelines by conducting the ETL processes helps the data to be cleaned and transformed so that Data Scientists can start to analyze it.
The importance of Data Engineering is thus the consistent data quality, scalability, and security that it provides for the organization’s data.
Key Skills to Look Out for When Hiring Data Scientists
Data Scientist skills include Technical, Business, and Soft Skills which are highlighted below.
1. Technical Skills
Among skills for Data Scientist, technical skills are the first priority to look at.
With various libraries such as Pandas, NumPy, and Matplotlib, Python helps in the entire data analysis process including data manipulation, cleaning, analysis, and data visualization.
- R Skills
R is a programming language designed for statistical computing that includes a huge collection of packages for basic to advanced data analysis.
- Statistics and Math
Due to the fact that performing analysis and using machine learning algorithms require a good grip of Statistics and Math, understanding of key concepts is crucial for a Data Scientist.
- SQL Skills
Structured Query Language (SQL) helps to manage and retrieve data from relational databases which most businesses rely on.
- NoSQL Skills
NoSQL skills are helpful to handle large amounts of complex, unstructured data through MongoDB, Neo4j, and Cassandra.
- Data Visualization
Since communicating the findings from the analysis to relevant stakeholders is vital, data visualization through libraries of Python and R as well as BI tools like Power BI and Tableau are a priority to assess.
- Machine Learning
Machine Learning is among the high in demand skills needed for Data Scientists since it has high impact business applications including creating recommendation engines, customer churn prediction, etc.
- Deep Learning
For advanced use cases such as image recognition, robotics, and autonomous cars, Deep Learning helps by creating powerful algorithms called artificial neural networks.
- Natural Language Processing
Natural Language Processing (NLP) helps to derive insights from natural language and texts. The best example of its application is ChatGPT.
- Big Data
Analyzing vast amounts of complex data which cannot be stored in traditional data processing software requires the use of Big Data. For instance, a Data Scientist working in healthcare would require a skill set on Big Data tools such as Hadoop and Spark.
- Cloud Computing
Without cloud computing, Data Scientists would have to wait hours to see the results of their model. It is also much more cost friendly for small and medium enterprises to conduct data science workflows through the cloud.
2. Business Acumen
The job of a Data Scientist is to solve business challenges. Consequently, in addition to the technical skills, Data Scientists need domain specific knowledge of the industry.
This includes an in-depth understanding of the customer journey and the business operations. Hence, consider Data Scientists who have solved actual problems within the organization.
3. Communication Skills
The lack of communicating findings that seem complex and technical to non-technical business stakeholders may hamper their comprehension and trust in executing the recommendations.
One specific skill to look at is Data Storytelling which is the ability to communicate the findings conducted through data analysis through visualizations and an interesting narrative.
4. Data Ethics Skills
Since the unethical use of data can lead to fatal consequences to the reputation of a company, Data Scientists need to be aware of principles in ethics of data.
Some key concepts to discuss include data privacy, algorithm bias, and model transparency
5. Environmental Awareness
It is no secret that Climate Change is among the top priority issues of the world today. The energy required in storing and processing big data comes at the cost of huge CO2 emissions.
Environmentally aware Data Scientists are more likely for ethical data handling and minimizing resource consumption in their analysis processes.
Key Skills to Look Out for When Hiring Data Engineers
To hire Data Engineers who can become a key contributor to your team, it’s crucial to assess the following Data Engineer skills.
1. Technical Skills
Python offers Data Engineers a rich ecosystem of libraries for cleaning, transforming, and enriching data along with helping build an effective data architecture.
A commonly used data pipeline tool is Hadoop which is written in Java. Thus, having a background in Java is helpful in using it.
A key role of a Data Engineer today is connecting the organization’s systems with cloud-based data sources.
Additionally, once a cloud data pipeline is set up well, it can scale without the Data Engineer having to make constant changes.
Kafka is an essential part of the Data Engineer skill set since it is a distributed streaming platform that helps data to move between different parts of the system with speed and reliability. It is a key enabler of real-time analytics.
Among the most commonly sought after skills among Data Engineers is SQL since it is used to modify relational databases along with optimizing the data infrastructure towards higher efficiency.
Organizations that require a scalable data storage solution need Data Engineers who can use NoSQL to store and process large unstructured or semi-structured data.
Different tasks such as data cleaning and feature extraction can be performed at scale using NoSQL.
- Data Pipelines
Data pipelines help to move data from one place to another. Usually, the raw data is transformed to a usable form and then loaded into the database.
Skilled Data Engineers create data pipelines which automate parts of the data movement helping save time and money.
- AI and Machine Learning
AI and ML help to automate time consuming processes such as data cleaning and transformation that lead to building data pipelines efficiently.
- Data APIs
Use of Data API’s help to easily connect the data from diverse sources such as a combination of databases, web applications, and cloud platforms.
- Data Mining Tools
Data Engineers with understanding of data mining tools are able to transform raw data to meet the requirements of such tools for structured data.
Moreover, they can design data pipelines through which data from different origins can be accessible without issues in the data mining tools.
- Basics of Distributed Systems
By distributing the workload across multiple computers, distributed systems have allowed processing and analysis of big data with speed.
Data Engineers can be asked on their understanding of its basics such as scalability, fault tolerance, and consistency.
- Data Visualization
Data Engineers can use data visualizations to make it easy in pin-pointing any data quality issues including any outliers or data transformation errors.
2. Critical Thinking Skills
Critical thinking skills enable analysis and evaluation of information in a meaningful way where specialists can make well informed decisions or judgements.
For example, such skills can be helpful for Data Engineers to evaluate the data infrastructure by considering various factors such as scalability, automation, and security risk.
3. Collaborative Nature
Hire Data Engineers who understand that building a great data pipeline is a team effort that includes multiple stakeholders. Inability to cooperate with other team members can lead to unnecessary conflict and delayed work.
4. Presentation Skills
Data Engineering is a cross-functional role which requires explaining technical concepts to non-technical stakeholders too.
Specifically, look into their ability to convey messages through data visualizations and provide a convincing argument for their ideas.
5. Problem-solving Abilities
Proactive Data Engineers are able to define the problem, find their root causes, and develop a relevant solution even in high pressure scenarios.
Consider adding scenario-based questions or real-world case studies to the interview to assess the approach the candidate takes in identifying the problem and coming up with a solution.
6. Interpersonal Communication
Interpersonal communication is not only about communicating well but also about active listening, empathy, and respect with the person you are interacting with in the workplace.
These are skills required for Data Engineer as common problems such as misunderstandings in requirements and need of rework because of lack of project updates can be avoided with them.
7. Good Time Management Skills
When Data Engineers have project delays in their work on developing a great data infrastructure, it creates a ripple effect where the work of Data Scientist’s may also be delayed.
Look out for candidates who have a sense of ownership in their work and have learned to prioritize important tasks to maintain efficiency.
Common Mistakes to Avoid When Hiring Data Scientists and Data Engineers
The competition to hire Data Engineers and Data Scientists high makes it important to avoid common mistakes in the hiring process.
1. Imprecise Job Title
The wrong job title can create two major problems:
- It may stop the ideal talents from applying to the position
- Talents who don’t align with your needs may end up being the majority of applicants.
Sorting through tons of applicants while not finding the right applicant wastes valuable time and energy.
2. Failing to Emphasize Interesting Problems
Dedicated Data Scientists and Engineers get a sense of fulfillment from solving complex issues.
Therefore, when crafting the job postings, mention the specific challenges that the talents will get to work on. For instance, for a Data Scientist role, mention the specific problem to solve such as financial fraud detection utilizing computer vision.
3. Undifferentiated Sourcing Strategy
The same sourcing strategy which works for other positions may not work well for hiring in these data positions.
One strategy could be to tailor the approach to attract diverse talents by looking beyond your current geographical location since remote work has now made accessing a global talent pool feasible.
Also, consider utilizing specialized platforms and networks to maximize the chance of a wider pool of qualified candidates.
3. Inconsistent Skill Validation Process
A common mistake is that the skill validation process is usually inconsistent across different candidates which leads to a wrong judgement of the talent’s skills.
To ensure a consistent process, consider defining a clear set of criteria for assessing the candidates’ skills and knowledge.
For instance, the skill validation process can be standardized with technical interviews, coding challenges, and a portfolio review which are quantitatively assessed.
4. Unicorn Hunting and Other Expectation Gaps
For the first time employers who are looking to hire Data Scientists and Data Engineers, having a job post up for months without getting the right candidate isn’t uncommon.
The reason is the search for a “unicorn” candidate who possesses every skill imaginable. But the market reality is that very few possess such attributes.
How to Hire Data Scientists and Engineers: Choosing the Perfect Candidate
The perfect candidate is out there who can be hired by following these steps.
1. Define the Role Clearly
Be detailed in defining the specific responsibilities and problems they will work on.
Additionally, it could be a good idea to provide context on what growth trajectory the company is aiming for and what will be the instrumental role of the candidate in achieving it.
2. Ensure the Appropriate Technology Stack
The technology stack that Data Scientists and Engineers work with is a key part of their daily work which is a priority they assess in the Job requirements.
As a result, examine the current tech stack and ensure they are the right set of tools and platforms which will enable the team to work efficiently.
3. Competitive Compensation Package
In a market with high demand for such talents, a compensation package which doesn’t meet the standards of the market is likely to be declined.
Create a compensation package which is balanced with competitive salary along with benefits such as paid time off, flexible working, and professional development opportunities.
4. Strategic Recruitment and Networking
Try pinpointing the channels and contacts who can help to source and connect with potential candidates. For example, Datacamp might be a channel to consider posting a job as data talents use it to grow their skills.
5. Consider Outsourcing
The hiring process can take months without a guarantee of finding the right candidate. As per a study, it takes 25 days to hire a Data Engineer and 23 days to hire a Data Scientist.
To solve such pain points, explore the option of outsourcing certain recruitment efforts to make your hiring strategy more effective.
6. Swift Hiring Process
Rather than waiting for the “unicorn” hire, maintain realism by prioritizing key skills while accepting that a 100% fit might not happen. This will help secure qualified candidates promptly.
How Much Does It Cost to Hire a Data Scientist?
The average annual salary of a Data Scientist in the U.S. is $124,000.
On the other hand, the average cost of U.K. Data Scientist’s is $61,000.
Some European countries are more affordable. For example, the average annual salary for Data Scientist at top tier companies is $46,000 in Poland.
Similarly, Ukraine has an average annual salary of $37,000 for Data Scientists.
How Much Does It Cost to Hire a Data Engineer?
The average annual salary of a Data Engineer in the U.S. is $117,000. The costs are lower in the U.K. at $65,000.
In Poland, the average salary for Data Engineers is around $39,000 which is also a similar rate in Ukraine.
Best Models for Hiring Data Scientists and Engineers
There are 3 models you can choose from depending on your needs to hire the right data talent.
An in-house model is hiring an employee as part of your organization.
- Long-term commitment: Full time employees who stay long-term can grow and become an integral part of your team
- Team Integration: They can work cohesively with other team members even in ad-hoc situations
- Direct Oversight: Maintaining quality as per company standards can be more effective with an in-house employee
- Higher Costs: They will require a monthly salary along with benefits and perks
- Longer Recruitment time: Finding the right candidate might take longer because of more filters in the hiring process
- Limited Expertise: In data roles where new skills and technology knowledge is needed, existing employees might face skill gaps
Freelance model engages an independent professional on a project basis.
- Cost Savings: Freelancers charge for the specific project only without additional benefits
- Immediate Availability: They are available within days to get the project started
- Flexibility: Their flexible working style enables them to adapt to your needs
- Confidentiality Issues: Working on data projects with freelancers may pose security concerns.
- Inconsistent Availability: Since they work simultaneously with multiple clients, they’re availability can be unpredictable.
- Limited Oversight: The lack of direct supervision may lead to quality issues.
Outsourcing involves contracting an external firm to perform the necessary tasks for your organization.
- Reduced Administrative Burden: These firms take care of payroll and benefits of talents involved in the project which enables you to focus on core business activities.
- Cost Efficiency: Payment only needs to be done for the services that are taken helping reduce other operational costs.
- Flexibility: Businesses can scale resources quickly based on project needs.
- No direct control: The processes are handled by the firm which may lead to gaps in quality.
- Communication Challenges: Gaps in coordination among the two parties may lead to misunderstandings and project delays.
- Hidden Costs: Though outsourcing can initially seem cost effective, some firms may have unexpected fees because of extended timelines.
Hire Offshore Data Scientists and Data Engineers
An alternative approach is to hire offshore Data Scientists and Engineers. Hiring offshore talents offers the opportunity to explore a global talent pool who can have the necessary skills at more affordable costs.
Why Hire Data Scientists/Engineers from Poland or Ukraine?
A key concern for employers is whether more affordable costs come at a compromise with quality.
This is a key reason why you may consider to hire Polish or Ukrainian Data Scientists and Engineers since these countries have become reputed for providing high quality with affordable costs.
Both of these countries have established themselves as leading IT outsourcing hubs in Europe along with strong data science R&D centers and impressive STEM education enrollments.
Additionally, the professionals here are well accustomed to Western business culture with fluent English.
Let RemoDevs Help You Hire Data Scientists and Engineers
RemoDevs can help you hire Data Scientists and Engineers with less stress since we take care of the first part of the recruitment process. We also take care of filtering out the top 3% candidates who will best fit your offer and company culture.
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