Building a data powerhouse starts with the right team, but this can be more challenging than it first seems.
Titles such as ‘Data Analyst’, ‘Data Engineer’, ‘Data Scientist’, ‘Data Architect’ (the list goes on) have been popularised, and differentiating between these roles can often feel like navigating a maze.
This can be made even more difficult due to conflicting priorities from senior management, or the limitations of recruiters – particularly in their lack of data expertise.
Whether you know data roles inside out, but still seem to get the wrong outcome, or if you need the nuances of each role demystifying, we can help.
In this article we’re going to focus on one of the most commonly misunderstood hiring questions: should you hire a Data Analyst or a Data Engineer?
Read on for a closer look at where analysts and engineers overlap, where they differ, and what key factors will determine the right fit for your data team.
Analyst vs Engineers: The Skills Overlap
The “hard” skills of analysts and engineers crossover in 3 main areas:
- Technical Skills
- Data Understanding
- Problem-solving
Overlap 1: Technical Skills
Both analysts and engineers regularly use programming languages such as Python and SQL for various forms of data analysis.
Engineers are more likely to leverage these languages for more customised automation tasks, while analysts use them for insight generation, (i.e. to uncover patterns and relationships)
Similarly, both analysts and engineers often use data manipulation tools to carry out their day-to-day responsibilities – analysts more frequently for data cleansing and exploration, engineers to optimise how data libraries are stored, linked and used.
Overlap 2: Data Understanding
Data analysts and data engineers both require a solid understanding of the fundamentals – such as data sources for usage, data quality for accuracy, and data architecture for ease of navigation.
Both roles require the ability to identify and address issues with the data as they arise.
While data analysts require a knowledge of the data sources they are consuming, engineers go beyond this, holding a much more comprehensive understanding of broader data sources and their integration methods to maintain and optimise a seamless data flow journey.
For both data roles, having clean and accurate data for reliable analysis is paramount – but data engineers have the additional duty of ensuring data quality through checks, cleansing routines and robust security practices.
Overlap 3: Problem-solving
Analysts and engineers share a fundamental ability to solve problems within data.
While there is a shared task of identifying anomalies and other issues with data, analysts tend to be more focused on quickly fixing those problems at source.
They act as detectives, examining the data for inconsistencies, missing information, or unexpected formats that could skew analysis. But they are not simply fixing data problems; they’re using data to solve problems.
Engineers on the other hand often delve deeper into the data ecosystem, mitigating systemic or upstream issues that disrupt the general flow of information.
They focus on bottlenecks within data pipelines and potential security threats, ensuring a smooth and seamless data flow from the source systems to the end user.
What sets analysts and engineers apart
Now we’ve covered where these roles overlap and the nuances within this, let’s look at the more clear-cut distinctions between the two.
The data analyst defined
Analysts are all about interpretation and communication.
The mission of a data analyst is to extract meaning from data. They’re the storytellers, using data to uncover insights and trends.
To do this, they use skills in analysis (obviously), data collection and cleansing, data modelling, and finally, reporting and visualisation.
Although the majority of data analyst roles might not hold the same level of technical depth as engineers, their required skills tend to be broader, requiring a stronger sense of general business acumen, data interpretation (i.e. what does the data really mean) and non-technical communication to and from end business users.
The data engineer defined
Engineers are all about the robust collection, management and delivery of data from its capture point to the end user.
The mission of a data engineer is to create and maintain data infrastructure for analysts and business services to utilise. They’re the builders, constructing pipelines so data flows smoothly to those that need it.
Data engineering demands skills in ingestion and integration, data warehousing and management, data modelling and optimisation, as well as knowledge of security, access control, automation and scripting.
Their responsibilities tend to be less business-centric than data analysts, but require deeper technical expertise. They need to think holistically about complex data problems, and be quick on their feet in troubleshooting data issues and inconsistencies as they arise.
“Should I hire a data analyst, or a data engineer?”: Final thoughts
When exploring how to close out any immediate skills gaps in your team – whether that be for an analyst, or an engineer – it’s important to focus on the individual candidate.
Consider all the skills and expertise they bring to the table and how they can round out your team’s overall capability, both now and into the future.
This means taking into account the less tangible attributes and soft skills as well.
Focusing on how a particular individual aligns with the direction of your business is always better than simply fulfilling an immediate need.
This approach of looking at the overall picture opens up opportunities for other team members to upskill through on-the-job experience or additional training.
For more on how to ensure you get the right fit, have a look at this article on steps to nail the next hire for your data team.
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