Analytics engineer: learn about 6 responsibilities of this new role

Analytics engineer is the newest role within a data science team .

And anyone applying for a job in analytics engineering needs to master techniques and processes involved in building a modern, robust and scalable data infrastructure to assume their responsibilities.

This is exactly what Indicium Academy students have been learning in the first Analytics Engineering Training in Brazil .

And if you want to get a job meeting the demands of modern companies that are investing in data professionals, start preparing now by learning about six of the responsibilities you will take on when you get there.

And when you need to ask questions about your projects, always count on our team for that. We have a community on Slack where we share a lot of our daily lives. Between here .

And now, happy reading!

6 responsibilities of an analytics engineer at Indicium

In short, as an analytics engineer here at Indicium , you will combine technical knowledge with strategic business notions to respond to some very important actions in order to achieve two main objectives:

• objective 1 - bring data closer to organizations to generate value.
• objective 2 - assist managers and leaders in making decisions faster and at lower costs.

As we teach at Indicium Academy , our course platform for data professionals , in this position, you must have a holistic view , that is, of the analytics process from end to end, knowing and mastering each step to be able to act on each one. .

Logically, the responsibilities of an analytics engineer are not limited to just six activities. And remember: each project will have its specificities. But the duties we list below are very relevant and basic for every successful modern data project. Therefore, these are the responsibilities of analytics engineers…

1. Provide data transformation

And this will be done following the most modern analytics approach, that is, through the systematic transformation process known by the acronym ELT (extract/extract; load/carrar; transform/transformar).

In this approach, the raw data is first loaded into a data warehouse and only then transformed, within the DW itself. Even though it is still a new methodology, this is already one of the most fundamental processes for analyzing and storing data.

And anyone who is an analytics engineer executes this transformation through SQL scripts managed by tools, such as DBT.

But, before this actually happens, specific tasks need to be planned and executed, some of them even several times. Are they:

map data for extraction - identify data sources and necessary tables. define the type of processing - choose already predicting how/when the data can be updated. analyze the volume of data - and also check the growth speed of this volume. check the policy security - and be very careful with access and processing of sensitive data, such as: personal, banking, industrial secrets, etc. define the type of infrastructure - whether it will be in the cloud, on-premises or hybrid. map ways of consuming data - structure how they will be consumed, whether in reports, BI tools, APIs, etc. From this cycle, the transformation itself begins. And who does all this? Who is an analytics engineer!

2. Manage data and cloud platforms

With the emergence of data warehouses and data lakes in the cloud, and the new approach to data transformation (ELT), analytics engineers also have in the scope of their responsibilities knowing how to store large volumes in a scalable way and manage them on cloud platforms. .

In our Analytics Engineering Training , we always recommend working according to the specific needs of each project, choosing between the main cloud computing providers: AWS , Google Cloud and Microsoft Azure .

3. Assist in data driven discussions and decisions

Responsibilities go beyond the techniques of modeling raw data into consistent information . To deliver them with richness and clarity of details to those who make decisions, during the life of the project, the communication of those who are analytics engineers cannot fail.

Therefore, to assist in data-driven discussions and decisions, interpersonal skills go hand in hand with analytical (and critical!) thinking.

You need to know how to ask the right questions to facilitate the interpretation of data and reduce the time for decision making.

And you also need to know how to describe very well the business rules that will make the project have a modern, robust and scalable data structure.

4. Create, monitor, establish and predict business and product metrics

Following the flow of responsibilities, analytics engineers must define the way of doing business through the rules they describe .

These rules are not created from scratch, but must reflect and consider existing processes , the company's internal policy and rules of conduct. In this process, another important responsibility is implicit: suggesting the standardization of indicators in all sectors of the business.

In short, to be able to create, monitor and then establish and predict new metrics for both business and products, it will be necessary to look for all this information to map it.

And to carry out this mapping, anyone who is an analytics engineer must know how to use analytical thinking tools , such as:

• the 5 Whys ;
• the Pareto Diagram ;
• the Ishikawa Diagram ; and the GUT Matrix.

5. Build dashboards in BI, data warehouse and ELT tools

Analytics engineer is also responsible for knowing some basic design principles and knowing how to use specific tools to create storytelling within a clear visual context that can be analyzed by ordinary people.

Nothing against reports in Excel, but here, in this function, visualization panels are presented - the modern and now famous dashboards . To carry out this task well, you need to understand the importance of data visualization and put good practices into practice!

It all starts with identifying the context and use: will what will be presented be explored or explained? After defining the questions that will be answered , analytics engineers move on to choosing the visual elements.

For analytics engineers who are just starting out, we recommend our tutorial available at this link . It's a very didactic step in 16 short, direct and complete videos.

6. Automate reporting

Finally, after intelligent and easy-to-understand reports have been created, following DataViz best practices , it is also the responsibility of analytics engineers to automate them with real-time notifications .

This is considered a major differentiator in business intelligence (BI) solutions because it eliminates hours of work that would be spent on preparing reports.

To carry out this automation, you will need to master tools such as:

• Google DataStudio
• Metabase
• Power BI
• Painting
• Qliksense
• QuickSight
• Mode
• Looker etc.

It is important to carry out prior study to get to know them and be able to choose the best one for a specific analytics engineering project . This includes doing a cost analysis as well.

Study to be an analytics engineer

Learn the main tools, techniques and methodologies with Indicium Academy in the first Analytics Engineering Training in Brazil.

Come and study with someone who knows how to do it and is a leader in data science and analytics in Latin America .

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