Como você mostra o valor da sua engenharia de dados?

By connecting the data engineering projects to the business initiatives or goals they are trying to enable/ achieve. This can give early buy ins and more confidence to the leadership teams. A longer term strategy would be to iteratively reduce technical debt in the form of siloed data, sunsetting inefficient data workloads, performance optimization that would have latent/ direct impact on the business outcomes

The most crucial value a data engineer can contribute to a team remains their ability to comprehend the business's problem, simplify it through close collaboration between the software and data science teams, and transform it into a feasible solution, often involves the creation of a data model or table.

Certainly, aligning your data engineering work with business goals is crucial for demonstrating its value. Here's my perspective on this matter: Understand the Business: Invest time in comprehending the core objectives, challenges, and strategies of your organization or client. Engage in conversations with business leaders and stakeholders to grasp their pain points and aspirations. Translate Business Needs into Data Solutions: Once you have a clear understanding of the business context, work on translating those needs into actionable data engineering projects. Collaborate with data analysts and domain experts to define the right data requirements.

Be close to the business areas; they can facilitate your job a lot and you them... Once you have established this bridge, you can show real business value to the stakeholders. Keeping the business goals connected to your roadmap or OKRs would be an excellent move.

Demonstrating the value of data engineering involves tangible outcomes and effective communication. I quantify improvements in data processing speed, reduced errors, and enhanced data accessibility. I showcase successful project deliveries, emphasizing their impact on business decision-making. Utilizing before-and-after metrics, I highlight efficiency gains and cost reductions. Creating insightful visualizations using BI tools illustrates data-driven insights. Regular communication with stakeholders ensures alignment with organizational goals. I also emphasize adaptability, showcasing how data engineering enables swift responses to evolving business requirements, ultimately proving its intrinsic value.

-Documenting and standardizing data engineering work is akin to building a sturdy foundation. It's not just about showcasing your expertise but ensuring the long-term success of data projects. -Clear documentation enhances collaboration, reduces errors, and accelerates decision-making. Plus, it's a testament to professionalism and commitment to quality.

Why is it important? 1. To ensure the reliability, scalability, and maintainability of data solutions. 2. To facilitate collaboration, communication, and knowledge sharing. 3. To showcase data engineering skills, best practices, and achievements. How to do it: 1. Use a consistent style and format. Be clear and concise. 2. Use diagrams and illustrations. 3. Keep it up-to-date. 4. Share it with others. What to document: 1. Data ingestion and transformation processes. 2. Data storage and processing architectures. 3. Data quality and security procedures. 4. Data governance policies and procedures. Benefits: 1. Reliable, scalable, and maintainable data solutions. 2.Improved collaboration, communication, and knowledge sharing.

Proper documentation is an absolute requirement for not only data engineering, but for every aspect of engineering work. Today you are building one pipeline and 5 months after the completion you are asked to bring an enhancement to it. You might have forgotten what you did and how you did it but if you have a proper documentation, it will be very helpful. If someone else is working on that work later, without documentation, it might take weeks for them to understand what is going on, they might not be aware of edge cases. So document everything you are doing.

Write down how you do your data work. Follow the same rules to keep things consistent. Document data processes, data sources, and data transformations for clarity.

Ninguém gosta de documentar, é chato e parece sem sentido, até o dia em que você precisa de uma documentação pra lembrar algo criado a anos atrás, ou dar continuidade a um projeto que ficou no meio do caminho. Uma dica para documentação, seja simples, seja diretivo, seja detalhista, seja o que for, mas seja, faça, comente o código, mostre como fazer alterações simples, ganhe tempo para você e sua equipe com uma boa documentação.

-Automation is the data engineer's best friend. By automating repetitive tasks, you free up time for innovation and complex problem-solving. It's not just about efficiency but also about reducing human error. -Optimization, on the other hand, fine-tunes data pipelines for peak performance and cost-effectiveness. Together, automation and optimization are the pillars of high-impact data engineering.

Whatever you are doing can be further optimized some more .... may be not today but 1-2 year down the line with may be some new tech, some new configuration. Create your work by parameterizing it so that you can just feed data to it and it would work opening the way of automation.

"Automate the Mundane, Empower the Business" Data engineering is essential for businesses of all sizes to derive value from their data. However, data engineers can often be bogged down by mundane tasks, such as data cleaning, preparation, and transformation. This can limit their ability to focus on more strategic and innovative work. One option is to automate data engineering tasks. This can free up data engineers to focus on more strategic work, such as developing new data pipelines and machine learning models. It can also empower business users to take more control of their own data, without having to rely on the data engineering team for every little thing. They will appreciate the control and transparency. Automate and Empower!

Pense em uma tarefa que você faz todo dia, toda semana, quinzena, mês... vale a pena criar um relatório para automatizar cada etapa da sua rotina tediosa. Já tive a oportunidade de automatizar tarefas de rotinas gerenciadas, além de ser um processo prazeroso é extremamente útil, por exemplo, você pode automatizar alerta de erros nos dados históricos usando média móvel para determinar possíveis erros nos dados de algumas tabelas grandes e importantes. Automatizar é ganhar tempo no que realmente importa!

Automate and optimize your data engineering work to save time, reduce errors, improve efficiency, and free up your time for more strategic and value-added tasks. Here are some examples: 1. Data pipelines: Automate the process of extracting, transforming, and loading data. 2. Data orchestration tools: Schedule and manage data pipelines. 3. Data quality tools: Clean and validate data. 4. Data backup and recovery tools: Back up and restore data. 5. Data monitoring tools: Track the performance and health of data pipelines and systems. Benefits: 1. Improved efficiency and effectiveness of data operations 2. Reduced risk of errors and outages 3. Demonstration of the value of data engineering to stakeholders

-Visualization and communication are the bridges between technical excellence and real-world impact. Visualizing data pipelines and outcomes helps non-technical stakeholders understand their value. -Effective communication ensures that your data engineering efforts align with organizational objectives. Sharing insights, challenges, and successes in a clear and relatable manner enhances collaboration and fosters trust.

Visualizing and communicating your data engineering work is an important part of your job. By doing so, you can show stakeholders the value of your work, build trust and credibility, get feedback, and share your knowledge and expertise with others. Here are some additional tips for visualizing and communicating your data engineering work: 1. Use storytelling to make your work more engaging and persuasive. 2. Use data to support your claims and recommendations. 3. Be transparent and honest about the challenges and limitations of your work. 4. Be open to feedback and suggestions.

Make pictures and reports to explain your findings. Use easy-to-understand charts and dashboards to show others what you're doing. Create visual representations of data trends and insights to make them accessible to non-technical stakeholders.

You need to use the appropriate language, level of detail, and format to convey your data engineering message clearly, concisely, and convincingly.

Learn and improve your data engineering work to stay ahead of the curve, deliver better results, and advance your career. Here are some tips: 1. Stay up-to-date with the latest trends and best practices. 2. Experiment with new technologies and tools. Seek feedback from others. 3. Analyze your own performance. Examples: 1.Read data engineering blogs and articles. 2. Attend conferences and meetups. 3. Contribute to open source projects. 4. Take online courses and tutorials. 5. Seek out mentors and coaches. Benefits: 1. Be more productive and efficient. 2. Deliver better results for your team and organization. 3. Advance your career and earn more money.

Stay updated on new tools and tricks in data engineering. Keep finding ways to do your work more effectively. Seek ongoing learning opportunities to enhance your skills and adapt to industry changes.

The best way to show potential business partners, external stakeholders, employers, clients, or collaborators your abilities and competence in data engineering, you must showcase your work by showing the "before" and "after" difference. This could be a positive financial impact or a non financial impact like improved customer satisfaction index kpi.

You need to showcase your work by creating a portfolio, resume, or profile that can highlight your data engineering projects, skills, results, and achievements.

Showcasing your work is not just about self-promotion; it's about building a strong professional presence. A well-crafted portfolio or profile can make you stand out in the competitive data engineering landscape. Actively participating in data communities, sharing insights, and collaborating with peers not only enhances your knowledge but also expands your network and reputation. It's a strategic move for long-term career growth.

Talk about your successful projects and how they help the company. Share stories and examples to show the real impact of your data work. Present case studies and testimonials to illustrate the value you bring to the organization.

Numbers are always best way to express value over text insights. Its must to include numbers to your insights by value or percentage.

One of the most important and influential topic in the last decade. I would to stress on laying the foundation of a concept "continuous engineering" in the data world.