You're a data engineer. How can you show your employer you're worth your weight in gold?

If you have to prove to your employer of your value as an employee…I’m afraid to say that you are working in a toxic environment. Your value should be known already, they hired you for it. If they give work that you are not capable of doing or that isn’t in your expertise, go ahead and learn to do the job that is asked but if you will be judged by your ability to deploy wings and fly across the city…I’m afraid you will fail

The best data engineers know that the real problems are not data, but business related. Too often, data engineers and other data workers focus on solving the "data problem" by building complex technology sets that build pristine data sets. Pristine datasets that misunderstand the business intent. To do the job exceedingly well, data engineers MUST understand that the real problem that data helps inform is a business process / strategy / financial question. Generally these questions have 4-5 deeper questions that need to be answered as well. Want to be a great data engineer? Show that you can ask the right questions to find the right business problem. Show that you can build pristine data sets / models on this problem.

The best way to shine in the field of data engineering (in fact in any discipline) but especially in tech, is simple through demonstrating results. Over the years I've found a lot of value from "basic" solutions to immensely innovative ones, but sometimes the pursuit of the latter takes us to unwanted places and absurd complexities in our solutions. My advice: Keep it as simple as possible and scale from there. There are many Data Engineering solutions that are as easily solved as a Bash script that has a cron that runs periodically. There are also processes that can solve 70% of the problem with a quick iteration, do it! and then evaluate how to solve the remaining 30%.

Sometimes it can be hard to know what are the business problems we, data engineers, can help solve. I'd say focus on your immediate customers: data analysts. For data teams already valued at the C-level (co-faciliators of regular business reviews), make sure data analysts are productive and can deliver insights in time. Besides critical data quality issues discovered by business users, nothing hurt more the data team than not being able to deliver insights and therefore miss business opportunities. Relevant and up to data tooling, reviewed process to eliminate pain points, regular trainings for easing learning curve on data stack. These are all important

While technical proficiency is essential for data engineers, it is equally important to possess strong business acumen and interpersonal skills. This allows you to understand the needs of stakeholders and utilize data engineering tools effectively to solve relevant business problems, rather than prioritizing the use of the most advanced tools and frameworks. Try to identify the pain points and challenges that your business or domain faces, and propose data solutions that can address them. Show how your data solutions can generate value, insights, and impact for your employer.

Being a data engineer is not easy. You have to be an all-rounder within the team. From using various tools and technologies to handling clients to deployment, You will have to manage all the tasks. Here are some of the philosophies I used to be a better data engineer - 1. Be a quick learner and always develop a mindset for learning new technologies. 2. Understanding requirements is one of the key factors of a great engineer. You don't have to say "Yes, Understood" all the time. 3. Always communicate with your manager. 4. Document everything, Not for others but for you. 4. Make a to-do list. Prioritise your work and try to finish them. 5. Read books. 6. Be happy. 7. Be kind to others, help others as much as possible.

As a data engineer, to communicate effectively, it's important to: 1. Use clear and concise language when explaining technical concepts to non-technical stakeholders, avoiding jargon and acronyms. 2. Listen actively to understand the needs and concerns of stakeholders, and ask clarifying questions to ensure understanding. 3. Provide visual aids such as diagrams, charts, and graphs to help stakeholders understand complex data and processes. 4. Be proactive in communicating progress, challenges, and risks to stakeholders, and seek feedback regularly to ensure alignment and satisfaction. Effective communication is critical to ensuring that stakeholders understand the value of data engineering initiatives.

I love diagrams to communicate effectively. Every meeting must end with a diagram. There are a lot of techniques and free tools to do it. In my case I use very often the UML language because can show the activities and routes of any process, and at the end , everything is a process. Diagrams also is a documentation, equally understandable by IT professionals and business users.

I believe effective communication is crucial for a Data Engineer, especially post-pandemic as remote work becomes more common. The ability to convey complex data concepts in a clear and engaging way is more important than ever. Luckily, with advanced tools for creating diagrams and presentations, it's becoming easier to present solutions clearly. We should capitalize on these tools to ensure our ideas are easily understood and impactful, bridging any physical gaps in today's hybrid work environments.

Effective communication is paramount for a data engineer. Clear and concise communication of technical concepts ensures understanding across all stakeholders. Actively listening, using visual aids, and maintaining transparency enhance collaboration and trust within the team. Documenting thoroughly facilitates knowledge sharing and ensures continuity of work.

Continuous learning is necessary for Data Engineers. As a Data Engineer, we need to stay updated with the new shiny tools, and the new framework everyone is raving about. If we are not stagnant and keep improving our knowledge, we can identify more opportunities to directly impact the business and add incremental value. Learning continuously also ensures we stay relevant with our tech stack.

One thing that I find helpful is to keep learning, and keep taking initiatives, keep yourself involved in brainstorming sessions. An idea might not be helpful today, but it can be the perfect solution in the next use case. Make yourself flexible enough to keep adapting to new data-strategies.

I suggest Online Learning Platforms such as: Coursera: Offers courses on data engineering, big data, and specific technologies like Apache Spark, Hadoop, and more, taught by instructors from universities and companies. Udacity: Provides nanodegree programs in data engineering, covering data modeling, cloud data warehouses, data lakes, and automation. edX: Hosts courses and programs in data science and data engineering from institutions like MIT, Harvard, and IBM.

Continuous learning is an important aspect to remain up to date in industry. The data engineering has taken a long way from Pl/SQL, ETL, ELT, big data pipelines, cloud services, etc. Now a days, leveraging data engineering with Gen AI would be very appealing to most of the customers.

I recognize that staying current with the latest technologies in data engineering can be daunting, but it's essential. I've found that LinkedIn ??is an excellent resource for keeping up-to-date, as many professionals frequently share insights and updates on trending topics in the field. Following a diverse range of experts and joining relevant groups can provide a wealth of information and inspiration. I recommend actively engaging with these resources, as they offer a practical way to continuously learn and adapt to the ever-evolving landscape of data engineering.??

To innovate creatively as a data engineer, the following steps can be taken: 1. Stay current with industry trends: Stay current with industry trends and emerging technologies to identify new opportunities for innovation. 2. Encourage experimentation: Encourage experimentation and risk-taking among team members to foster a culture of creativity and innovation. 3. Collaborate with cross-functional teams: Collaborate with cross-functional teams, such as product management and design, to identify new use cases and applications for data. 4. Leverage data analytics and visualization: Leverage data analytics and visualization tools to identify patterns and insights that can inform innovative solutions.

Innovative thinking is the fuel that propels data engineers forward. Embrace creativity to devise novel solutions to complex problems. Don't be afraid to explore unconventional approaches or technologies. By thinking outside the box, you can push the boundaries of what's possible in data engineering and drive meaningful progress for your organization.

As a data engineer you should have really strong analytical skills, you are dealing with our organisation's data. Data engineers are really busy, but take the opportunity to derive insights on top of your data, you are in a good position to make a great impact in your organization.

From my perspective, a GitHub portfolio is key for showcasing data engineering skills. Start with simple, well-documented projects to highlight your mastery of best practices. This approach has been a game-changer in demonstrating my ability to innovate and communicate complex concepts effectively. Do you think I'm right??

I agree that managing projects professionally is crucial for data engineers. Planning, organizing, and monitoring tasks to meet standards is key. Using a methodology like DataOps can greatly help in tracking and improving project outcomes, making processes more efficient and effective.

As a data engineer, to manage professionally, it is important to establish clear expectations and goals, communicate effectively with stakeholders, prioritize tasks based on business value, and continuously improve processes and workflows. Additionally, it is important to stay up-to-date with industry trends and best practices, and to foster a culture of collaboration and innovation within the team. Effective management also involves regularly monitoring and evaluating team performance, and providing constructive feedback and recognition to team members. By following these practices, data engineers can ensure that their team is delivering high-quality work that meets business objectives and drives value.

Managing professionally as a data engineer involves taking ownership of projects, meeting deadlines, and communicating effectively. Prioritize tasks, delegate when necessary, and maintain professionalism in all interactions. By managing projects efficiently, you demonstrate reliability and contribute to the overall success of the team.

These days the data projects are very dynamic, the architecture decisions changes throughout the implementation of the projects. So, in order to deliver successfully, keeping the communication live about the tasks is very important. Also, active collaboration and advising the client for any possibilities of improvement in implementation and data quality measures will help to build a robust data pipeline with minimal surprises. One more thing I would stress is that data engineers should work on to build their second brain so that they don’t have to remember all the critical information, this will free up their mind for creative and robust work.

You should emphasize the significant impact data engineering has on achieving business goals: Revenue growth: data engineering optimizes the infrastructure for targeted marketing, increasing sales and revenue. Efficiency: streamlining processes and ensuring data accuracy improves operational efficiency and reduces costs. Decision making: Timely insights enable strategic decisions and improve competitiveness and market positioning. Risk mitigation: Strong data governance practices minimize regulatory risks and protect against data breaches. Competitive advantage: The use of data technology promotes innovation that leads to unique products and services and ultimately to a competitive advantage.

The one and only thing you need to know is your data. Spend time with it, look at the numbers and check if it makes sense. If your data is reliable, you will always be the first choice for any critical project. We underestimate the amount of time we spend cleaning and organising data when it is not vetted properly in the beginning.

Take the initiative to lead projects that have strategic importance to the organisation. Successfully delivering projects that align with business goals can showcase your leadership and technical skills.

Using state of art technologies seems very exciting, but the focus always should be the business necessities. Try to make it simple.

The one and only thing you need to know is your data. Spend time with it, look at the numbers and check if it makes sense. If your data is reliable, you will always be the first choice for any critical project. We underestimate the amount of time we spend cleaning and organising data when it is not vetted properly in the beginning.