5 Reasons Why Startups Should Hire a Data Engineer Instead of a Data Scientist

Last Updated on November 13, 2023 by Owen McGab Enaohwo

Data engineer vs data scientist: who should you hire?

Nowadays, Big Data and advanced analytics are crucial tools for online businesses like retailers and travel applications. Improving data analytics can potentially increase a company’s operating margins by over 60%. On the other hand, industries like the healthcare sector and companies like Athena Provider, can reduce 8% of their costs by improving their data storage systems.

Image Credit: datacamp.com

However, a question remains. Which is the best role to hire to stand out from competitors and break into the market? Between data engineers vs data scientists, which is best? Millions of companies believe data engineering is a crucial field, however, the best solution always depends on your business goals. As Big Data is a vast and evolving industry, it can be challenging to identify the right professionals.

Let’s look at the different roles and responsibilities in data science to understand why, as a startup owner, you should prioritize hiring a data engineer instead of a data scientist.

Chapter 1: The Hierarchy of Data Science

From targeting marketing campaigns to improving a product, Big Data processes are essential to improve a company’s services and increase leads. The first step to hiring the right candidate is to identify where the role will work in your company:

Role	Responsibilities	Aim
Data Infrastructure Engineer	Instrumentation, logging, sensors, external data, user-generated content	Collect
Data Engineer	Reliable data flow, infrastructure, pipelines, ETL, structured and unstructured data storage	Move/Store
Data Scientist/Data analysis	Cleaning, anomaly detection, prep Analytics, metrics, segments, aggregates, features, training data A/B testing, experimentation, simple ML algorithms	Explore/Transform Aggregate/Label Optimize/Learn
Machine Learning Engineer	AI, machine learning	Interpret/Analyze

The process involves four primary roles for each main phase: a data infrastructure engineer, a data engineer, a data scientist/analyst, and a machine learning engineer. If a company handles large-scale prediction and automated data sets, it’s wiser to consider hiring a specialized professional for each phase, from collecting data to analysis. A more effective option for smaller businesses or startups is to hire one person to cover both roles.

Chapter 2: Why Should You Hire a Data Engineer vs A Data Scientist?

For smaller companies and startups, hiring a new technical professional is a delicate process. Building a new team with limited resources requires careful consideration of the roles that can make a difference for company growth. Hiring a data scientist instead of a data engineer is a risk for early stages startups.

This makes sense because startups are trying to minimize costs–being early in the game. However, in a broader perspective–everything is data-oriented nowadays. Whether you’re a larger corporation with multiple clients or just a simple startup, the larger portion of the way businesses operates revolves around observing, analyzing, and interpreting data.

If you are launching a startup, let’s consider the core reasons why you should think to hire a data engineer instead:

Not Enough Data

Generally speaking, small/medium-sized companies and startups don’t have the proper setup to hire a data scientist simply because they don’t have enough data. Creating a complex data flow and databases requires time and a reliable method to collect data. In an early startup stage, a data engineer can handle a data scientist’s tasks by building the company’s data infrastructure, simplifying your team’s work.

Setting Data Flows

Besides not having enough data, startups entering the Big Data industry need an experienced data engineer to collect efficiently, store and analyze data. Hiring a data scientist without a solid methodology means hiring unnecessary team members, wasting the company’s budget and your team members’ time.

Tasks Overlap

Even for some large-size companies; it can be challenging to fit the role of a data scientist with other team members. A data scientist expands the work of a data engineer. Without enough data or a solid structure, the risk is hiring an overqualified candidate overlapping with a data engineer’s tasks. So, it is essential to understand the role of a data scientist before considering hiring one.

High Costs

In the US, the average annual salary for a Data Scientist is $142,258/year. With higher qualifications like a Ph.D. or MSc, the base compensation jumps to 150k-200k/year. For an early-stage startup, this average salary highly impacts the company’s budget, especially if the role isn’t necessary. On the flip side, hiring a data scientist offering a lower wage brings other risks. You might attract professionals like a data analyst or research assistant who won’t get any real value to the company. One solution to give yourself more bang for the buck could be hiring remotely and recruiting from a lower wage economy than the U.S.

So, suppose you’re hiring a remote data scientist. In that case, you might feel that an outsourced data science company is the most cost-effective solution. However, you don’t directly manage the development process, only on the outcome.

For this reason, an outsource data science company isn’t an effective way to understand the impact of the role on your company’s performance compared to an in-house data engineer.

Alling Team Members

Finally, a data scientist closely works with data engineers, team members, clients, and stakeholders. When you build a team to launch a startup, aligning team members is initially the most complex challenge. Hiring an unnecessary role will lead to misunderstandings, and tasks overlapping, making it harder for your team to align and improve the overall performance.

To recap, when it comes to data science and engineering, it’s crucial to value your company size to understand which role would benefit your company the most. If you need support with creating dashboards, tables, pipelines, reports, APIs, and automation, hiring a data engineer is the most cost-effective solution in the long run. When you need to underpin A/B testing, machine learning research, optimizations, or design AI pipelines, it will be time to add a data scientist to your team.

Let’s have a closer look at their responsibilities and why you should opt for a data engineer instead of a data scientist.

Chapter 3: Data Engineer vs Data Scientist: What Do They Do?

A data scientist’s primary goal is to aggregate and optimize data based on the new information. A data scientist’s learning background includes:

Computer programming
Statistic and linear algebra
Machine learning and algorithms

Data engineering aims to efficiently move and store data to facilitate and speed up analysis and optimization. A data engineer works with:

Big data storage and processing
Data pipelines
Model ETL (Extract, Transform, Load)

Let’s put in comparison data engineers’ and data scientists’ responsibilities:

Data Engineer’s Responsibilities

In short, data engineering relates to developing, testing, and maintaining database and large-scale processing system architectures. A data engineer can specialize in three main areas:

Generalist: Usually, in small teams, generalists supervise the whole data process, from data management to analysis. The role is generally the best option for the transition between data science and engineering.
Pipeline-centric: Common in mid-size companies, pipeline-centric data engineers, collaborate with data scientists to facilitate the process of data aggregation and labeling.
Database-centric: In large-scale organizations, engineers manage massive data flow and database analytics, working with data warehouses and developing table schemas.

Besides the final specialization, here are the primary data engineer’s responsibilities:

Developing, testing, and maintaining system architectures and data set processes
Aligning data architecture with business requirements
Monitoring data acquisition
Improving data reliability, efficiency, and quality
Conducting market and business research
Addressing businesses’ issues with large data sets
Deploying analytics programs, machine learning, and statistical methods
Setting data for predictive and prescriptive modeling
Identifying hidden patterns and automated tasks
Delivering updates, analytics, and report to stakeholders

Data Scientist’s Responsibilities

Named the ‘sexiest job of the 21st century by the Harvard Business Review, data science involves numerous applications and tools. These include data mining, statistical techniques, algorithms, and machine learning principles. The goal of a data scientist is to identify trends and patterns from raw data to support business strategies.

The primary responsibilities of a data scientist are:

Identifying relevant data sources according to a company’s goals
Collecting structured and unstructured data
Sourcing missing data
Organizing and labeling data into usable formats
Building predictive models and machine learning algorithms
Improving data collection processes
Processing, cleansing, and verifying data
Analyzing data via various methodologies to derive themes, trends, and patterns
Setting data infrastructure
Developing and maintaining databases
Assessing data quality removing or cleaning scripts
Generating insights from data sets
Reporting progress and outcome to executive and project teams
Creating data visualization

Chapter 4: Data Engineer vs. Data Scientist: Technical Skills in Comparison

Based on their responsibilities, a data engineer and a data scientist develop different and overlapping skills alike. Here is a comparison between the skill set of these two roles:

Data Engineer vs. Data Scientist: Technical Skills Set

Data Engineer	Data Scientist
Database systems (SQL and NoSQL): Data engineers manipulate database management systems (DBMS), creating an information storage and retrieval interface.	SQL databases: Data engineers use SQL to manage and query data held in relational database management systems.
Python, Java, and Scala: A data engineer needs to store these programming languages to connect databases with interactive and functional websites.	Python programming: A Data scientist needs a strong knowledge of Python and Pendas (Python data analysis library) to handle data mining, website construction, or running embedded systems by manipulating, reading, aggregating, and visualizing data.
Basics of distributed systems: Hadoop fluency is an essential requirement for data engineers. The Apache Hadoop software library is a framework to process large data sets with simple programming models.	Hadoop platform: Hadoop open-source software allows data scientists to process large datasets across computers’ clusters via simple programming models.
Data warehousing solutions: Handling Storing huge volumes of current and historical data for query and analysis, a data engineer handles several sources like a CRM system, accounting software, and ERP software. In addition, you should expect a familiarity with Amazon Web Services (AWS).	R programming: Mostly in an academic context, data scientists use R integrated software facilities to manipulate and calculate data creating a graphical display.
Machine learning: Data engineers need a basic knowledge of machine learning to make predictions, produce models and build accurate pipelines.	Machine learning and AI: Although not necessary, a basic understanding of machine learning and AI highly improves data scientists’ performance when using algorithms and data-driven models.
ETL tools (Extract, Transfer, Load): The ETL process organizes data from various sources, transforming data into a database or a business intelligence platform.	Data visualization: Data scientists visualize data through visual elements like charts, graphics, maps, infographics, and more, to illustrate the technical analysis to clients and stakeholders.
Data APIs: To access data, a data engineer uses APIs to read databases, retrieve relevant tables, process requests, and return an HTTP-based response to the web template.	Business strategy: Data scientists use data analysis to enforce a business’ strategy.
Algorithms and data structures: A data engineer usually focuses on filtering and optimization. However, a basic knowledge of algorithms improves the overall organization of data structure.

Some technical skills overlap in data science and engineering, while others highly differ. The primary difference between these two roles is that a data engineer analyzes, stores, and transforms data. In contrast, a data scientist optimizes and organizes these findings, creating visual representations for clients and stakeholders.

Finally, as both roles deal with other team members, stakeholders, and clients, soft skills like communication, collaboration, and presentation are crucial for a productive and effective workflow.

Chapter 5: How To Hire a Data Engineer

1. Create a Detailed Job Ad

As we mentioned, a data engineer can specialize as a generalist, pipeline-centric, or database-centric. For early-stage startups, the most suitable role is a generalist data engineer who can supervise the whole development process and create a solid methodology to handle data. So, in your job description, be as precise as possible on the tasks and goals you expect from the role. In addition, clearly illustrate your company culture and mission to attract candidates interested in your industry – rather than getting any job.

2. Target The Right Job Platforms

Covering databases and data flows, the role of a data engineer is crucial for dev teams and front-end design. Unlike data scientists, an in-house full-time contract is the most effective solution to ensure continuity on the project and build a solid data flow architecture. And remote hiring is the most reliable option to reduce recruitment expenses and hire skilled candidates. Targeting timezone-friendly areas, you can hire a remote Data engineer who will grow with your company and team.

Here are some of the most reputed job platforms to share full-time remote offers and find talented professionals:

Remote.co
FlexJobs
DistantJob
We Work Remotely

3. Interview Rounds

Especially if you don’t have the support of expert recruiters, don’t underestimate the interview process. It is essential for startups and smaller teams to hire candidates who fit with the team daily and collaborate with other team members. We always recommend at least three interview rounds:

Technical skills: The first round focuses on testing technical skills. Among specific questions about technologies you will need for your project, you can test their abilities and delivery time with whiteboard tests or trial tasks.

Soft skills: Once you select candidates based on technical skills, the second step is having another conversation to value communication and collaboration skills, future ambitions, and work ethic to understand the extent to which the candidate is willing to grow and stay in your company.

Team Interview: Finally, with the last 3-5 candidates, it’s wise to have a final interview with other team members. Here you can see how candidates interact with others and picture how an ordinary working day will go to make the final decision.

Conclusion

To recap, hiring a data engineer for early-stage startups is the most suitable and cost-effective option in the long run. To build a productive team, it’s wiser to focus on fewer team members, train them, and create smooth workflows to provide a quality product. After that, when your company grows and your data sets increase, you can consider adding a data scientist to support your data engineer’s work and analysis.

Author’s Bio

Costanza Tagliaferri is a Writer and Content Marketer at Distant Job. She has covered a wide range of topics. Now, she is focussing on technology, traveling, and remote work.