What are the top things to do to get promoted as a Data Engineer?

In addition, understand the business problem you are trying to solve so you can come up with the best architecture that can grow with the business, as well as create test cases and meaningful demos for business stakeholders to sign off on.

Mastering data engineering coding is crucial, as is demonstrating ongoing progress through research, improvement, and refinement. Mastery involves not only speeding up tasks but also assisting others in the field to achieve their goals.

I agree that knowledge is crucial. You should be familiar with architectural patterns, tools, and functionalities applicable in each scenario to comprehensively address real business challenges. However, it's impossible to know everything in detail. The advice is to select the tools you want to delve into and study them extensively, preferably at least one for each stage of implementing a structured approach in data engineering. Despite mastering the concepts, always remember that there's no substitute for hands-on experience.

Most important skills are SQL, Data Modelling, any of the programming languages such as Python or Scala. Apart from this understanding architecture of your storage and processing engine be it Oracle , Redshift , Spark or any other is very critical. Before moving on complex topics, focus on simple concepts. For example, how does join work with multiple values, what are jobs , tasks and executors etc. One of the best ways to learn is to pick the longest running component of production pipeline and try to optimise its performance as much as possible. While this is a time consuming process it helps a lot in understanding queries, storage and processing. This will be 10x better than doing any courses outside.

Tenha pleno dominio de aplica?oes de dados de ponta a ponta, desde a parte de desenvolvimento a parte de deploy, a parte de ensinar sobre as aplica??es. Esteja sempre no radar de outros times e outros lideres, muitas vezes quem trabalha com a engenharia de dados é invisivel ou confudido, por isso sempre esteja presente para que seu trabalho tenha um nome associado, o nome também se refere ao seu time. Aprimore sus conceitos e suas habilidades tecnicas ao maximo.

Get a professional data engineering certification from one of the data platform providers, e.g. - Databricks Certified Data Engineer - AWS Certified Data Engineer - Microsoft Certified Azure data Engineer etc. Preparing for the exam takes some time, but you learn all the practical aspects of data engineering . And there are many good certification preparation courses on Udemy

In my journey as a full stack data scientist and data engineer, I've focused on key areas to stay ahead. Integrating Retrieval-Augmented Generation (RAG) models into data architectures has been crucial, enhancing processing with advanced natural language understanding. I've also explored the significance of vector databases for efficient NLP, essential in managing the surge of unstructured data. A major learning point has been blending unstructured and structured data, a skill critical for unlocking comprehensive insights and developing advanced analytics. Keeping pace with these evolving trends is vital for any data engineer looking to remain at the forefront of innovation and tackle complex data challenges.

You should be able to identify business bottlenecks, if possible reach out to your downstreams ie Data Scientist or Analytics Team and understand the pain points which can be solved at Data Engineering level.

Fuck certificates and do stuff instead. Code on your free time, set up a database on a Raspberry Pi. Do some Python shenanigans to collect or process data to it for fun. Learn how to set up Kubernetes on a virtualbox. Why? Youll have no use for it but you will have tried to learn something on your own and used your brain instead of waiting to be spoonfed on a course that will be 99% wrong for real world applications at best. All Google certs are pointless, their documents is more than you need.

Mantenha um apetite voraz por conhecimento! Devore novas ferramentas, frameworks, padr?es de projeto e código. Desafie-se em certifica??es de nuvem e na linguagem que "coda" sua trajetória. Participe de hackathons para ser um verdadeiro "guru dos dados". Pense até mesmo em fazer um MBA em engenharia de dados para se destacar ainda mais!

Destaque-se como o capit?o do seu squad. Apresente posicionamentos assertivos e assuma responsabilidades nas entregas do projeto. Se o código apresentar desafios, enfrente-os com maestria, sem receios de perguntar e buscar solu??es. Busque sempre entregar com alto valor, deixando a marca de qualidade em cada feito!

As a founder and leader of data teams, I've learned that taking on more responsibility as a data engineer means blending technical expertise with business insight. It's about understanding how analytics and data strategies align with business goals and ensuring that both structured and unstructured data are integrated for comprehensive analysis. This role demands not just software engineering skills but also effective communication, making complex data concepts understandable across the organization. Leading in this capacity has underscored the importance of being a connector between the technical and business realms, driving impactful data solutions that fuel company growth.

Dont do this. At all. If you are there test stuff during work hours make sure you have an acceptance from manager to spend time on it. Heck even make a ticket! People who just go off on their own and just "do stuff" without communication are counter productive.

Do the proper mapping of the business requirements like a skilled cartographer, because without a clear map, even GPS gets lost. Understand customer pain points collaboratively and create active problem-solving solutions. Be the architect of a well-guided project where every line of code tells a success story. Document your code step-by-step, tell an engaging narrative for when you leave the project. Leave a legacy that speaks for itself!

Each project has its own complexity and impact. Data engineers should focus on high impact projects instead of intuitively going for complex projects. No matter how complex problem one has solved unless it has resulted in high impact it will not matter to leadership. Also, quantifying impact of the project is equally important. It should be crystal clear from the beginning how much money or hours you are saving. This builds a very strong use case for promotion as compared to any other project which have been very challenging but didn’t result in substantial growth of the organisation.

I couldn’t agree more with you on this, know the business requirements are way more important than coding, because sometimes people think that data engineering is just moving data around, but is more than that.

Mapeie os requisitos de negócio como um cartógrafo habilidoso, pois sem um mapa claro, até o GPS se perde. Compreenda as dores do cliente de forma colaborativa e crie solu??es ativas na resolu??o de problemas. Seja o arquiteto de um projeto bem orientado, onde cada linha de código conta uma história de sucesso. Documente seu código passo a passo, conte uma narrativa envolvente para quando deixar o projeto. Deixe um legado que fale por si!

I've learned the importance of not only technical expertise but also adaptability and communication, especially when dealing with non-technical stakeholders. Each problem presents a unique challenge that often requires translating complex data insights into solutions that are understandable and actionable for everyone involved. This means actively listening to the needs of various departments, understanding their specific challenges, and then articulating how data can help meet these challenges in a way that resonates with their perspective.

One of the most liberating things I heard in my career, was that I was expected to fail 50% of the time. If I always failed then we had a problem because I clearly didnt know what I was doing. But if I succeded all the time we had equally big problem because I was not pushing my boundaries and making progress, neither for me nor the company.

A inova??o e a experimenta??o s?o essenciais, pois promovem uma boa cultura em rela??o às solu??es de dados, ent?o sempre esteja aberto a novas ideias, buscando feedback sobre sua evolu??o e integridade do que foi desenvolvido. Além disso, é importante ter criatividade e curiosidade, pois quando você se desafia a algo novo, você é capaz de adquirir conhecimentos que est?o fora do dia a dia de um Engenheiro de Dados, o que agrega muito para sua carreira profissional, principalmente quando você consegue compartilhar isso em um ambiente de trabalho em equipe.

Navegue pelas novidades do mercado como um explorador ávido. Realize provas de conceito, apresente solu??es inovadoras para desafios e desafie sua mente a encontrar caminhos alternativos. Seja colaborativo e compartilhe essas solu??es com seus colegas, construindo uma atmosfera de crescimento conjunto. Inova??o é a chave para abrir portas e criar um ambiente que prospera com as ideias de todos!

Innovation and experimentation in data engineering are crucial, and a key aspect of this is learning to fail fast. In my role, I've embraced a culture of continuous innovation, where experimenting with new technologies, methodologies, and ideas is the norm. This approach involves rapidly prototyping solutions, testing them in real-world scenarios, and being prepared to quickly iterate or pivot based on the outcomes. Embracing failure as part of the learning process is vital; it allows me to identify what works and what doesn’t more efficiently, leading to faster improvements and more robust solutions.

Images convey a wealth of information. Let's simplify the jargon and communicate in plain English. The most effective way to attract attention is to keep things simple. "Speak to children in their language, and they will learn with ease." - Unknown

- Soft skills are the most underrated when it comes to professional growth while having the most impact. - It is 15-20% technical skills and about 80% your professional, soft, communication and people skills that matter. Here are few suggestions to improve soft skills: 1. Take some online courses. 2. Practice active listening. 3. Do regular exercise. 4. Meet new people. 5. Engage in networking events. 6. Believe.... and you are half way there.

I am amazed at the fact that no one mentioned the most important determinant of promotion in a corporate setup - having the 'right' boss. The right boss recognises your strengths and provides opportunities for you to shine because of them. At the same time, he understands your weaknesses and gives you an environment where you can improve upon them by watching others who are better at it than you are. No one comes equipped with all the strengths that are mentioned in the various points of this topic. People excel in some and suck at others. The right boss knows this and figures out a way for you to move ahead despite all this. Of course, getting the 'right boss' isn't under our control. Which is why when you get one... treasure it.

IMO if you can make your down stream consumer life easy, meaning they can consume data without worrying about quality & usability. If you solve that promotion and paychecks will follow.

Para ascender como #ingeniero de #datos, es crucial dominar profundamente el ámbito técnico y actualizarse constantemente con nuevas #habilidades y tecnologías emergentes. Asimismo, asumir más responsabilidades y resolver problemas #empresariales complejos son fundamentales para el crecimiento profesional. La capacidad de #innovar, #experimentar y aplicar soluciones #creativas se vuelve vital para enfrentar desafíos en constante evolución. Además, desarrollar habilidades blandas como la #comunicación #efectiva y el trabajo en equipo complementan el perfil integral que se requiere en este rol, lo que demuestra una visión más amplia y estratégica en la resolución de problemas.

1. Good backend development skills. 2. Proactive in work. 3. Good communication & Soft Skills. 4. Keeping your visibility game strong.

To get promoted as a data engineer, focus on: Aligning your work with business goals. Solving key business challenges. Continuously improving technical skills. Taking initiative and leading projects. Collaborating and helping colleagues. Seeking and acting on feedback. These steps can help you demonstrate your value and readiness for promotion.