In recent years, one of the hottest careers has been data science. High market demand, high pay, a hefty paycheck, and a glamorous work title all encourage recent graduates or those wishing to change careers to pursue careers in this field. However, the job descriptions and workload distribution in a data team could be confusing; sometimes, even the applicants remain confused about the specific roles they are applying for. The worst-case scenario can involve wasting a lot of time, applying for jobs one is not qualified for, and eventually losing out on jobs.

Data analysts and data engineers are the key players in a data science team. Both the roles are equally important when it comes to extracting insights from data; however, it is necessary to know what exactly differentiates a data analyst from a data engineer.

Now, the very first requirement is to understand the journey of data. 

• Data recording: Whenever an individual visits a website, their browsing behaviours are tracked and monitored. It can be anything on how much time they spent, what they looked for, what they enquired about, how much they spent, etc. 
• Data Storage: This recorded data is usually sent to a place (usually a cloud) like the servers of AWS, Azure, Google Cloud or some on-premise servers.  
• Data transfer: Data at this stage is not even structured and can be termed raw data. In order to acquire insights from the data, it needs to be available for the query. Hence, the raw data is now transferred to a database. Different databases exist depending on the requirements and capabilities required by different firms. Some of the popular ones include Microsoft Access, MySQL, Oracle database, MongoDB, and more.
• Extracting data value: Here, data cleaning and transformation take place. Two significant ways that exist today are ETL, i.e., extracting the data, transforming it, and loading it into a database. Nowadays, ELT is preferred with data extraction, loading it into a database and then transforming data depending upon the requirement of the firm. Thus, the process provides clean, validated and usable data.

With this usable data in the database, one can either analyse it to gain valuable insights or build some ML models. Furthermore, once this entire flow of data is clear, it will be easy for us to differentiate a data analyst from a data engineer.

So, who is a data analyst

Data analysts are a group of people who organise data to find trends that can aid in decision-making. Getting good operational performance depends heavily on data analysts. Their capacity to sift through enormous amounts of data, provide KPIs that are relevant to business, uncover insightful information, and guarantee that the organisation gets its numbers correct is crucial. To be precise, any analytics project starts with raw data, and it’s the job of data analysts to:

• Mine data from primary and secondary sources.
• Understand and interpret data to solve business problems with the help of statistical tools.
• Remove noise to provide clean data.
• Gain relevant insights from data and give recommendations that can aid in the expansion of firms using their technical and right skills for the job.
• Create dashboards with good visualisation to convey the message and make non-tech players of the organisation understand the scenario. 

To give an example, suppose an XYZ store wants to understand its customers and wants to segment its customers based on factors including brand loyalty and the money they spent. Now, a data analyst will look at the data and identify the trend. 

• Let’s say the first group of customers (A, B, C) visit the store frequently, but this trend gets reversed in the last few months. Now, either they decided to shop at a competitor’s brand, or they don’t require products at all.
• A second group (D, E, F) are not regular customers but visit whenever there are some discounts and promotions on a product.

Now, reaching and regaining these groups of customers requires different approaches that can be discussed in a team meeting. This is generally how a data analyst works.

Then, what does a data engineer do

Think about how the data analysts get the data required to make insights and recommend steps for the future? Think about the starting point when a consumer lands on a website (data recording) to the point when data is actually stored in the database for use; who makes all this possible? To be precise, it is the job of a data engineer to take care of data till the time it reaches the database. Moreover, in a data science team, data analysts and data scientists require data to work upon, and a data engineer sets up the entire structure. They design database schemas, prepare data pipelines to manage data flow, and carry out quality checks to ensure the data is accurate. 

Some of the main tasks of a data engineer include:

• Ingest data from multiple sources and provide it onto a single platform for analysts and scientists to carry out their tasks.
• Ensure deploying the most cost-efficient and optimised data pipeline, data structure and data storage technique.
• Ensure the availability of updated, validated and accountable data for data analysts and scientists to work upon. 

Take a look at the different skill sets required for data analysts and data engineers:

Source: Edureka

To conclude, the data lifecycle values data engineers and analysts equally. For organisations to develop data-driven decisions that create economic value, competence in each of these fields is necessary. Similarly, the aspirants must understand each job’s requirements to find the most suitable roles they want to take in the future.