You're a data visualization specialist who wants to become a data engineer. What's the best way to start?

Assessing your skills is paramount when transitioning from a data visualization specialist to a data engineer. Reflecting on my own experience, I would emphasize the importance of identifying overlapping competencies such as programming languages, database management, and data modeling. For example, as a Data Visualization Specialist, my proficiency in tools like Tableau and Power BI aligns with foundational skills required for data engineering. By recognizing these overlaps, I can strategically focus on enhancing complementary skills, like Python programming and SQL, ensuring a smoother and more effective transition into the realm of data engineering.

To transition from a data visualization specialist to a data engineer, begin by mastering programming languages like Python and SQL, essential for data engineering tasks. Gain proficiency in database management systems and ETL (Extract, Transform, Load) processes. Acquire skills in big data technologies such as Apache Spark and Hadoop. Pursue relevant courses, certifications, or a formal education in data engineering. Apply these skills to real-world projects, collaborating with data engineers, to gain practical experience. Networking, attending industry events, and staying updated on emerging technologies will also enhance your transition to a data engineering role.

Transitioning from a data visualization specialist to a data engineer involves a shift in skills and knowledge. Start by understanding the responsibilities of a data engineer, encompassing data architecture, ETL processes, and database management. Learn programming languages like Python or Java, focusing on data structures and algorithms. Acquire knowledge of databases, including both relational and non-relational types, and develop proficiency in SQL. Explore big data technologies such as Apache Hadoop, Spark, and Kafka. Familiarize yourself with cloud platforms like AWS, Azure, or Google Cloud, as well as ETL tools like Apache NiFi or Talend.

Identify your level of expertise; then, you know where you should begin. In other words, "know where you are, to know the direction you need to go to get you to your destination."

Start by evaluating your current skill set. As a data visualization specialist, you likely have a solid understanding of data analysis, visualization tools, and possibly some programming languages like SQL or Python. Identify the gaps in your knowledge related to data engineering, such as database management, ETL (Extract, Transform, Load) processes, and familiarity with big data technologies.

Apply your existing skills to create projects that showcase your ability to work with data. Transition smoothly by incorporating elements of data engineering into your visualization projects. For instance, demonstrate your understanding of data pipelines, data warehousing, or data modeling. Highlight any experience with tools like Apache Spark, Hadoop, or cloud platforms like AWS, Azure, or GCP.

Showcase personal projects that you have done on LinkedIn, Github for example; and if you need project ideas there are many Data Engineering projects available online.

Converse com um engenheiro de dados, e descubra as principais skills que você deve aprender. Planeje projetos que necessitam dessas skills. E ent?o execute esses projetos do início ao fim e divulgue em seu portfolio público. Você ganhará duas vezes. Primeiro, por aprender. Depois, porque esse portfolio te ajudará a conseguir oportunidades de emprego. Talk to a data engineer and discover the main skills you should learn. Plan projects that require these skills. And then execute these projects from start to finish and publish them in your public portfolio. You will win twice. First, for learning. Second, because this portfolio will help you get job opportunities.

Talk to your leadership about this and ask for guidance on how to do it internally I firmly believe it's a smarter move to seek a career transition first within the company you are employed. If that doesn’t work out, move to plan B and apply to positions on the market. Your current boss can guide you on how to do it and also give you career advice that will assist you in doing it in a more assertive manner. Also, talk to people who are in the position you want and ask for advice.

Arguably, the most crucial. In other words, you need to learn, and learn from competent individuals who have "battle scars", to allow yourself to grow quicker in the area, which in this instance is "data engineering". So upskill vertically and horizontally to put you in a better position!

I sometimes see junior members of my team transitioning from data visualization to data engineering. It often feels like a natural evolution if you want to know everything about data.. As you dive deeper into data viz, you uncover the nuances of data quality and structure. You start exploring the transformation layers, understanding how data is collected, cleaned, and processed. This curiosity leads you into the realm of data engineering, where you focus on optimizing data pipelines and ensuring data integrity.

Naturally as you spend more time in Data Visualisation, you'll be exposed to many Data Engineers. Your position as a Data Consumer is a massive advantage here. Pay attention to what makes an Engineer great or not great to work with. What do you wish they did differently? Why did they approach a task a certain way? How did they foresee issues you had not considered? Start to pose these questions to the Engineers you find are great to work with - this is a great way to work on the soft skills required to be an Engineer, before you need to learn about tooling.

Hoje, a maior dificuldade n?o é obter informa??o, e sim obter informa??o de qualidade. O mesmo vale para fontes de estudo. Por isso, costumo dizer que conversar e aprender com outros engenheiros de dados n?o é o terceiro, mas o primeiro passo para o especialista em visualiza??o de dados que quer se tornar engenheiro de dados. Assim, já saberá pelo e por onde come?ar a estudar. Today, the biggest difficulty is not obtaining information, but obtaining quality information. The same goes for study sources. Therefore, I usually say that talking with and learning from other data engineers is not the third, but the first step for the dataviz who wants to become a data engineer. This way, you will already know how and where to start studying.

Start applying for entry-level data engineering roles or internships. Tailor your resume and cover letter to highlight transferable skills and projects that demonstrate your commitment to transitioning into this field. Prepare for interviews by studying common data engineering concepts, algorithms, and practical scenarios. Emphasize your adaptability and eagerness to learn.

Pensar em projetos desde seu objetivo até o seu fim (último produto a ser entregue) e ent?o planejá-los em engenharia reversa também pode ser um bom come?o para ajudar um especialista em visualiza??o de dados a se tornar engenheiro. Primeiro, pensa-se no dashboard que se quer construir. Depois em como o conjunto de dados deve ser. E depois no que é necessário para o obter esse conjunto de dados. Thinking about projects from their purpose to their last product, and then planning them in reverse engineering can also be a good start to help a data visualization specialist become a data engineer. First, think about the dashboard you want to build. Then, how the dataset should be. Then, what is needed to obtain that dataset, and there it goes