What do you do if you receive feedback as a data engineer?

In my view, it is an important aspect of professional growth and development. Some attitudes and actions help make you effective when receiving, such as: When receiving feedback, listen carefully, without interrupting, Approach feedback with an open mind, recognizing that it presents an opportunity for improvement, Take time to reflect on the feedback and analyze its validity, Express gratitude to the person providing feedback for taking the time to share their ideas, and reciprocate by offering constructive feedback to your peers and colleagues.

I think this applies not only to Data Engineers but also to other professionals, perhaps due to the nature of articles generated by AI. One method I use a lot is paraphrasing. Practice your active listening by paraphrasing or summarizing the key points of the feedback to ensure mutual understanding. Paraphrasing can convey empathy and validation to the speaker. When you paraphrase, you are not only restating the speaker's words but also acknowledging their perspective and sometimes most importantly their feelings.

In my view, in order to use feedback more effectively, some actions are necessary, such as: Listen carefully to what is being said without interrupting. Note the specific points raised and try to understand the perspective of the person providing the feedback; Approach feedback with an open mind and a willingness to learn and improve; Take some time to reflect on the feedback you have received; Break the feedback down into actionable steps or goals that you can work on; Put the feedback into action by implementing suggested changes or improvements; Express gratitude to the person who provided the feedback, regardless of whether it was positive or constructive.

Bare minimum is Focus and Listen, if possible take notes and try not to think of solution when getting feedback. My recommendation for the feedback is to listen carefully and understand the context, situation as well. Feedback may change based on situation. Feedbacks are key inputs in system improvement. Understanding the bigger picture is very important, so it is ok to politely ask about the situation, context, what customer (or who is giving feedback) was trying to do and how they see it in idea environment. As far as possible try not to think about solution when receiving feedback or start defending.

Reflecting objectively on feedback is crucial for personal and professional development as a data engineer. I take the time to assess the feedback received, considering both its strengths and areas for improvement. Through this process, I gain valuable insights into my performance and identify actionable steps to enhance my skills further. By maintaining objectivity, I can implement necessary changes with clarity and purpose, ultimately contributing to my growth and success in the field.

I'll rephrase it differently: reformulate my understanding of the problem in objective terms that correspond, on one hand, to my understanding of it, and on the other hand, in formal terms that contain the premises of a formal/semi-formal modeling, thus based on precise and objective terms. From there, I have 1/ started to demonstrate competence to my interlocutor (I listened to them and I understood, or I tried to), 2/ the means to validate that the problem is well posed, or a basis from which the interlocutor will be able to rectify or specify it. In other words, without a few back and forth exchanges like this aimed at posing the problem, which is the first essential step to solve it properly.

Receiving feedback as a data engineer is essential, by approaching feedback with an open mind and a willingness to learn and improve, professionals can leverage it as a valuable tool for actionable items. One should Listen attentively to the feedback without becoming defensive. If the feedback is not clear, one should ask for more information to understand it fully. One must reflect on the feedback and determine specific actions that can be taken based on the feedback. Feedback should be taken as a continuous process for growth and development. Seeking feedback regularly is recommended.

After receiving feedback, it's essential to engage in reflective analysis, maintaining objectivity by separating personal feelings from the professional content of the feedback. This entails considering the validity of the points raised and comparing them with one's own observations of the work. Reflection allows for a comprehensive digestion of the feedback, enabling evaluation of its implications without being clouded by initial emotional responses. This process aids in identifying actionable insights from the feedback while discerning subjective opinions, empowering individuals to make informed decisions on how to improve their work effectively.

Once you have all the details, take time to objectively assess the feedback. Separate your emotional reactions from the factual content of the feedback. Look for recurring themes or specific areas where improvements are suggested. This can help you prioritize the most impactful areas to address.

Enganam-se as pessoas que acreditam que a dúvida é o melhor caminho. Se ficou com dúvidas, não hesite em perguntar. Afinal, estamos buscando a nossa melhoria! Pense e coloque em prática!

In the realm of data engineering, clarifying doubts is paramount for ensuring accuracy and efficiency in our work. I approach doubts with a proactive mindset, seeking clarification through thorough research and reaching out to knowledgeable peers or mentors when necessary. By addressing uncertainties promptly, I uphold a standard of precision and quality in my projects. Embracing the opportunity to learn and refine my understanding, I cultivate a solid foundation for success in my role as a data engineer.

A lot of time people tend to refrain from asking questions. Asking question is the best thing to do. Imagine leading a project and in the middle of development you realize that you have assumed many things which leads to delay and escalations. It is best that all such doubts are cleared at the initial stage. Keep things on email for record and have all the stakeholders looped in for any important updates.

If I receive feedback as a Data engineer, I would plan responsibly by first acknowledging the feedback Then, I would carefully analyze the feedback to understand the specific areas for improvement. Next, I would develop a detailed plan outlining steps to address the feedback, including acquiring new skills, refining existing techniques, or seeking mentorship. Finally, I would track my progress regularly and be open to further feedback to ensure continuous improvement in my role as a Data engineer.

If any part of the feedback is unclear, don’t hesitate to ask for specific examples or clarification. This ensures that you fully understand the feedback and the expected outcomes. For complex issues, seek guidance or suggestions on how to improve. This could be from the person providing feedback or from colleagues and mentors.

In response to feedback as a data engineer, I believe in planning responsively. This involves carefully analyzing the feedback received and developing a structured plan of action to address any areas of improvement. By prioritizing tasks and setting achievable goals, I ensure that each step taken is purposeful and aligned with the overarching objectives. Flexibility is key, allowing me to adapt my approach as needed to achieve optimal results. Through responsive planning, I strive to continuously elevate my performance and contribute effectively to the team's objectives.

As a data engineer, receiving feedback is vital for professional growth. It's crucial to actively listen and understand the feedback, reflect on its implications, and seek clarification if needed. Taking actionable steps based on the feedback, whether it involves adjusting technical approaches or communication styles, is essential. Seeking support from colleagues or mentors can aid in addressing feedback effectively. Following up with the provider of the feedback and treating it as an ongoing process ensures continuous improvement in one's skills and performance as a data engineer.

After planning the changes, it is a good practice to discuss again to check if the changes are proper and if there is any other better way to solve the problem.

Receiving feedback as a data engineer is invaluable for professional growth. It is crucial to approach feedback with open mind, actively listen, and seek clarification when needed. Reflecting on the feedback allows for a deeper understanding of areas for improvement which can then be translated into a responsive action plan. This plan should include specific steps, measurable goals, and timelines for implementation. By actively soliciting feedback, implementing changes, and celebrating progress, we can continuously evolve and excel in our roles. Embracing feedback as a catalyst for growth fosters a culture of learning and adaptability, ultimately leading to enhanced performance and success in the field of data.

Based on the feedback, outline specific steps you will take to address the areas for improvement. Set realistic deadlines for these actions. If there are multiple areas to address, prioritize them based on impact and urgency. Focus on changes that will have the most significant positive effect on your work.

Executar as alterações propostas é um ótimo retorno. Pode ser que no começo seja difícil e você não concorde, mas a área de dados precisa de conversa entre seus pares, então execute! Se falhar, amanhã ou depois você corrige e aprende com suas falhas. Agora, o que não vale é não executar por medo de falhar.

The actionable part of managing feedback comes with implementing communicated changes. There may be code refactoring, updating data contacts and documentation, and all of this should be monitored for each change made. Keep an open line of communication as changes are made and tested.

Upon receiving feedback as a data engineer, I swiftly implement necessary changes with precision and dedication. I approach this task methodically, ensuring seamless execution while maintaining the integrity of existing systems. By promptly incorporating feedback-driven modifications, I demonstrate my commitment to delivering high-quality results and exceeding expectations. Through this proactive approach, I contribute to the continuous improvement of projects and contribute to the overall success of the team.

The technical aspect can take 10 minutes or 10 years. If the client trusts my expertise, the important thing is to be able to tell them whether I can solve their problem or not. If I can but it would take me 10 years, it's better to tell them that I can't. If I can but in a month, it all depends on what they are willing to pay me. If I can do it in 10 minutes, they will be less interested in the fact that I perfectly solved their problem almost in real-time.

Start working on the changes or improvements you’ve planned. This might involve learning new skills, altering your approach to tasks, or seeking more regular feedback on your progress. Keep a record of the changes you make and the outcomes. This documentation can be useful for future feedback sessions or performance reviews.

After making the necessary changes, follow up with the individual who provided the feedback. This could be through a demonstration of how their suggestions were implemented or a summary sent via email. Following up demonstrates your commitment to feedback and allows for any final adjustments or approvals. It effectively closes the loop on the feedback process and sets the stage for ongoing professional development and collaboration.

After implementing changes, ask for feedback again to ensure your actions are effective. This shows your commitment to improvement and helps you gauge the success of your efforts. Use the new feedback to make further adjustments. Continuous improvement is a key part of professional development in data engineering.

Always approach feedback with an open mindset. Try to clarify things, if you have doubts on anything. Always identify your weaknesses and work on them to improve your performance. How you approach feedback is an important aspect of not only data engineering but also all the fields out there.

Having an open mindset is most necessary to consider while receiving feedback. It is an opportunity to reflect upon ourselves and improve where it's necessary.

First of all, I believe it is important to thank for the feedback if it has been given in good faith, because it is an invaluable tool for all of us to improve. Even if it has not been given in the best manner, sincere feedback will allow us to identify aspects of ourselves that we may not be paying attention to or prioritizing in the way that would be appreciated by others. Even if we do not agree with the feedback we receive, it allows us to learn more about the values of those evaluating us and improve communication in the future.

Feedback is not a negative thing, infact it means others are interested in the work output we did as Data engineers and there are opportunities to improve that pipeline by utilising the feedback. Feedback is part of continuous improvement cycle and should be taken in that spirit with incremental improvements done to achieve the target objectives

I am big on learning. As such, I document my growth process. Whenever possible, document your process from feedback to final resolution (technical or otherwise). You may need to refer back to it in the future or it may come in handy for someone else.