My routine as an Analytics Engineer
Tags: data, analyticsengineer, dataanalytics, datascience
Published: 2024-03-31 01:40:04

In a world increasingly driven by data, the Analytics Engineer plays a fundamental role. This hybrid professional figure combines data analysis, data engineering, and communication skills to transform raw data into insights that can drive business success. The goal of this post is to document my daily routine and typical tasks in this profession.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/u9cnrjiaqv829ks73u45.png)

## What are the responsibilities of an Analytics Engineer?

The image above humorously depicts the role of a professional who is increasingly in demand in the data field. This role, Analytics Engineer, provides many opportunities to develop technical skills and collaborate on business projects at the same time, as it is a more "cross-functional" position. The idea is to be able to bridge the gap between the data area and the areas that need to make data-driven decisions.

### The most common responsibilities include:

- **Data modeling:** This involves designing and implementing data structures and schemas to store and organize data.

- **Writing SQL queries:**This involves using SQL to extract, clean, and transform data.

- **Creating and maintaining data documentation:** This involves documenting the data sources, structures, and transformations.

- **Communicating results to business teams:** This involves presenting data in a clear and concise way that can be easily understood by business users.

- **Collaborating with data team members:** This involves working with other data professionals to collect, clean, analyze, and visualize data.

An Analytics Engineer works with Data Analysts and Data Engineers to provide simple visual representations of data patterns and conclusions and communicate their meaning to internal and external stakeholders, colleagues, and end-users.

## My daily routine

To surprise no one, a large part of my tasks revolves around writing queries and cleaning data that will be used in the data visualization stage. But I'll dedicate a topic just to explain this.

In the data visualization part, I focus on creating dashboards in Tableau to assist decision-making in various areas of the company: Customer Success, Operations, Product, People, etc.

Another activity I'm involved in, is meetings with the business team and the research team, with the aim of learning more and more about the product, new features, and being able to generate insights with relevant metrics to help in the scenario where the team needs it, and also to act as a "bridge" to foster communication between product and data.

I also engage in creating and maintaining documentation (database, processes, tools, new features, etc.), validating prototypes from the design team, and now I'm learning to create pipelines in Databricks🤩.

## The SQL Universe

In 99% of the time when I need to fetch data for a team or when I receive a dashboard request, I use SQL to clean and structure the data in the granularity and format I need. Honestly, I've been enjoying the experience! Understanding the context and the format of each metric within the query is priceless!

At first, it was quite challenging to understand the structure, syntax, where each data comes from, and how the database tables communicate, but in 3 months (doing complex queries every day, at least 6h/day), I managed to get the hang of it. In the end, the syntax is not so hard, and you get used to it gradually.

Something that helped me a lot in the beginning was to see examples of queries and ask ChatGPT to explain line by line of a complex query that wasn't clear to me. Another wonderful strategy was learning to use Common Table Expression (CTEs). Man, the query readability is another level! It's great for organizing everything, and also for ensuring the quality of maintenance later, especially when you add multiple contexts in a single query.

## Constant Evolution

The field of Analytics Engineering is relatively new and is constantly growing. I believe this hybrid role will be increasingly sought after by companies, so here's a tip for all the data analysts out there: learn SQL😄.

New technologies and tools are constantly emerging, requiring us to stay updated, especially in the data field where reliability in the data we provide is crucial. This constant evolution ensures that our careers are challenging and rewarding, offering opportunities for continuous learning and professional growth.

If you are also working in this field, want to work in it, or have any questions regarding this article, feel free to reach out to me on [LinkedIn](https://www.linkedin.com/in/mouralet/)!

What will I learn tomorrow? Where will all of this take me? Stay tuned and find out. ツ