Data Engineer vs. Data Analyst: What’s the Difference?
Data engineer vs. data analyst: who does what? Which should you hire first? How does each position fit within a wider team?
If you’re starting to build a data strategy and are a little uncertain over where the boundaries of each role lies, don’t worry – you’re not alone. As more businesses turn towards big data-driven strategy and decision making to increase revenues and improve performance, the ‘data engineer vs. data analyst’ question comes up a lot. It’s one of our biggest FAQs.
That’s why we’ve created this top-level guide to both data engineers and data analysts, their roles and how they work together. We’ll discuss overall focus, day-to-day tasks and the skills each position needs and, later on, your hiring options.
So, without further ado…
What Do Data Engineers Do?
As both individuals and businesses, we generate more data than ever before. That data contains a huge amount of commercial value…if you have the capability to unlock it.
All too often, data sits in silos that aren’t accessible organization-wide. And, without proper data processes, the potential for data duplication, corruption or loss increases. This both reduces the value you get from your data and increases the chance of a breach (and by extension, fines from regulatory bodies, other legal action and reputational damage).
That’s where data engineers come in.
Data engineers design, build and maintain the data structures needed to securely store and process data at scale. Ultimately, this allows you to transform huge amounts of raw data into a resource that people across your organization can access on demand.
The result? Your employees can make better decisions based on accurate, real-time data, and you get significantly greater insight into how you’re performing across all major KPIs. As a starting point, data engineering allows you to:
- Optimize marketing campaigns based on customer data
- Identify user experience improvements
- Develop new products and train AI/deep learning tools
- Create accurate forecasts and inform strategic decision making
- Identify areas for operational improvements
This is why data engineers should be the first data professionals you hire. Data analysts won’t have data to work with without the appropriate pipelines in place, so it’s vital you build that infrastructure before investing more heavily in the analytical side of your strategy.
What do Data Engineers Build?
Data engineers spend a lot of their time building pipelines that make data accessible for everyone that needs it.
These pipelines take raw data into a data repository (such as a data lake or data warehouse), where it can be stored and processed into useful reports. These pipelines typically take one of two forms:
Exact Transform Load (ETL) pipelines transform data before it enters the repository to fit with a specific schema. This is a great option for repositories that run off a relational database structure, like data warehouses.
Exact Load Transform (ELT) pipelines load data into your repository in its original state, so that it can be transformed when queried. If you’re storing huge amounts of both structured and unstructured data (such as in a data lake), ELT pipelines help you do this without sacrificing speed.
On top of this, data engineers are also involved in:
- Creating analytics tools that allow data analysts to interpret data for product development and business strategy purposes
- Creating and optimizing relevant datasets for end users (a data engineer that focuses exclusively on this task is known as an ‘analytic engineer’)
- Creating APIs so that data can flow across your organization unhindered, without getting stuck into silos
- Looking for improvement areas in current data structures and optimizing them for long-term scalability
- Creating and implementing good data security policies across your organization
What Does a Data Analyst Do?
The best data structures in the world hold no value if no-one’s using the data they provide.
Whilst your data engineers may have a solid understanding of the basics of data analysis, they won’t usually have the experience for anything more complex. And, given how demanding the rest of their role is, they certainly won’t have the time.
If you want to maximize the value you get from your data pipelines, you need someone (usually a team of people) that can dedicate the majority of their time to interpreting data and using this to inform strategy.
A data analyst (or ‘big data analyst’ as they’re often known) works with data as their full time job, using data to answer questions and solve problems. In a business context, this means identifying, collecting and interpreting data for commercial benefit.
Data analysts work with three main types of data to gain insights into commercial strategy. These are:
- Descriptive analysis explores what has happened in the past – “turnover increased this quarter, both year on year and in comparison to the previous quarter.”
- Diagnostic analysis explores why it happened – “data suggests this could be because of a bounceback from a COVID-related slump combined with our high profile advertising campaign.”
- Predictive analysis explores what will happen, based on trends in existing data – “turnover will continue to increase year on year but overall profits will temporarily slow due to a seasonal downturn and a planned IT restructure.”
How do Data Analysts Work?
Broadly speaking, data analysts follow a five-step process to analyze trends and solve business problems using data:
- Identification of issue: What question are you looking to solve for your business? To answer this, what do you need to measure and how will you measure it?
- Collecting the raw data sets needed to answer the question – either from internal sources like your CRM or external sources (e.g. government records, social media APIs)
- Cleaning the data for accurate analysis. This may involve removing duplicate data points, identifying anomalies, standardizing data format and correcting syntax errors
- Using data analysis techniques and tools to identify trends and correlations that might be useful in answering your identified problem.
Interpreting the results of analysis against your original question. Are there any recommendations you can make based on the data, and what limitations are there to your conclusions?
Pipeline created by data engineers make this process more efficient by:
- Providing real-time access to relevant internal datasets and ensuring their quality
- Automating data cleansing processes on large datasets
As a result, it’s likely that your data engineers and data analysts will collaborate closely on identifying which data pipelines to build and how they should function.
So…Data Engineer vs Data Analyst: What’s the Difference?
If you’re looking for a snappy, single sentence summary: a data engineer builds data structures whilst a data analyst uses data to solve problems.
Even simpler: an engineer builds. An analyst…well…analyzes.
Whilst there might be some crossover in knowledge, these are two separate functions that it is difficult to combine, as each has a focus that is fundamentally different from the other.
That said, data analysts and data engineers will need to work really closely with each other to ensure that analysts have access to the data they need, 24/7. This might involve building new pipelines, or modifying existing ones to cover new datasets.
What Skills Should I Look For In Each Role?
Both data engineers and data analysts should have brilliant problem solving skills. Don’t dismiss ‘soft skills’ either – both roles sit within highly collaborative teams, so team working and hitting deadlines are a must. You can be a technically competent engineer or analyst without people and organizational skills, but you can’t be a great one.
It’s in the application of these problem solving skills that the roles differ. Coding will likely be significantly more central to data engineers than analysts, as their main role is building and automating data pipelines. You should also look for more extensive development experience, such as API development and database modeling.
Data analysts, on the other hand, are much less code heavy. Their focus is on in-depth analysis. Look for numerical naturals, with plenty of statistical analysis experience across previous positions. If you’re hiring grads, a mathematical background is a huge plus – or you could offer a skills test at interview to tap into previously hidden talent.
Data Analyst vs Data Scientist vs Data Engineer: What’s the Difference?
If you’re looking at creating a team of data experts, you might have also come across the term ‘data scientist’. What are they, and how do they fit into the team structure?
Typically, data scientists design new processes for data modeling and production using prototypes, algorithms, predictive models, custom analysis and machine learning. These can then be used by data analysts to analyze large sets of data as efficiently and as accurately as possible.
So, to fit them into the team:
Data engineers build the pipelines and infrastructure that allows data to flow across your organization. Building automated data pipelines and maintaining the structures that surround them are a key part of a data engineer’s role.
Data scientists then use this infrastructure to build innovative predictive data models, which analysts can use to gain greater insight into how the business can perform better. Often, they use advanced machine learning processes to do so, so knowledge of AI programming is a must here.
Data analysts interpret the data provided by data pipelines and the models created by data scientists to interpret data. They then use this knowledge to inform strategy and suggest courses of action most beneficial to the company.
Where to Hire Data Engineers and Data Analysts
As more companies turn to Big Data to identify performance improvements, opportunities and areas for growth, data professionals have become hugely in demand across the labor market.
This means that in-house hires tend to be time consuming and expensive. You’re looking at an average salary of $112,000 for a data engineer, or $70,000 for a data analyst according to Glassdoor.
Whilst this investment is absolutely worthwhile if you see an internal data team as fundamental to long term success, it can slow down your data analytics strategy significantly – particularly if you can’t find good-fit hires.
If you want to kickstart your data strategy straightaway, you can outsource to a third-party agency like Tivix. We maintain a global network of data engineers, scientists and analysts that we can scale immediately for your project, whether you’re looking to permanently outsource or hand over to an internal team in the long run.
On top of this, we offer:
- Over a decade’s experience as a fully integrated software development agency
- Experience with clients big and small, across a range of industries – though if we had to drop a few names, we’d mention the UN and Tesla to start
- A range of associated services including project management and full stack development to give you flexibility over your project
We love talking all things data – why not get in touch today for a chat?
We’ll keep it low key, avoid overwhelming you with tech jargon and perhaps suggest a couple of next steps, if you want to talk more.