What does a development engineer do?

The data engineer Is the professional responsible for Process and maintaining an easy way to the data, Creating Data Lakes, ETL Pipelines to handle this data, and Rest APIs to be consumed by the data scientist On the Other Hand, the Data scientist Uses that clean data to train ML models and create predictions, generating visualizations and having the skills to present them to the Leaders, Creating a Data Driver Environment

I always explain it as in a kitchen environment: Data Engineers: Prepare and maintain the "kitchen" and "ingredients" (data infrastructure and data). Data Scientists: Use the prepared "ingredients" to create "meals" (insights and models) that meet the customers' tastes (business needs).

Data Engineering professionals serve as the first point of contact for collection, integration and transformation of data produced from diverse systems, they in simple terms produce raw value from noisy and dirty data. Data science professionals are charged with analyzing, defining and modelling outputs from data engineering processes to draw insights that help answer questions posed by the business analyst/business

A engenharia de dados é responsável por garantir o adequado fluxo de dados na empresa, construindo repositórios de armazenamento dos dados, sua transforma??o e disponibilidade, através de bancos de dados ou API's, para posterior análise pela figura do cientista de dados. Esse processo é conhecido pela sigla em inglês ETL, que significa Extract, Transform, Load.

In my view,A development engineer is responsible for designing, developing, testing, and implementing new products and systems. They work in a variety of industries, including software development, mechanical engineering, and electronics. Development engineers use their technical expertise to ensure that products and systems meet the needs of customers and are efficient, reliable, and cost-effective. They may also work with teams of other engineers, designers, and project managers to complete projects on time and within budget.

In my experience, most of the time, engineers are responsible for building ETL or ELT, extracting data from different sources and cleaning the data to make it ready for data scientists or analysts. This process is usually done in the cloud (azure, gpc or aws), so it's important to understand the general concept of cloud computing before delving into a specific cloud.

Absolutely, data engineering is a broad field and professionals in this area often specialize in specific aspects depending on the organization's needs, personal interests, or industry trends. If you're particularly interested in maintaining high standards of data, you might choose to specialize in data quality, like myself.

Engenheiros de dados s?o responsáveis por garantir a disponibilidade de dados em hora e volume adequados para a sua análise. Isso é feito através da constru??o de pipeline de dados (captura, armazenamento, transforma??o e disponibiliza??o) e automatiza??o de processos ETL por meio de orquestradores de dados. Alguns orquestradores populares s?o o Apache Airflow e Azure Data Factory.

Hoje as minhas principais atribui??es s?o: 1.Coleta e Prepara??o de Dados: Os engenheiros de dados focam na coleta, ingest?o e prepara??o de dados. Eles trabalham para garantir que os dados estejam disponíveis, sejam de alta qualidade e estejam prontos para análise. 2.Infraestrutura de Dados: Desenvolvem e mantêm a infraestrutura necessária para armazenar, processar e acessar grandes volumes de dados. Isso pode envolver a configura??o de bancos de dados, pipelines de dados e outras ferramentas. 3. Eficiência e Escalabilidade: O objetivo é criar sistemas eficientes e escaláveis para lidar com grandes conjuntos de dados. Isso inclui otimizar consultas de banco de dados, implementar pipelines eficientes, etc.

Data engineers play a pivotal role in shaping the data landscape of a project or organization. Here's a friendly expert take on their responsibilities: Crafting Seamless Data Pipelines: As data engineers, you're the architects of smooth data flow. Crafting and testing pipelines isn't just about functionality; it's about creating a symphony of data that moves effortlessly. Designing Optimized Data Storage: Implementing and optimizing storage solutions is like painting a masterpiece, ensuring that every piece of data finds its perfect place. Championing Data Quality and Security: Ensuring data quality is like tending to a garden, and safeguarding it is like standing at the fortress gates, letting in only the trustworthy.

Data engineers should stay up-to-date on cloud technologies to make informed decisions about which approach to use, such as whether to go multicloud, or the difference between Microsoft Azure and Microsoft Fabric.

Data Engineering does have a lot of tools and technologies involved and one such popular tech stack which I personally like is comprised of the following: 1. Apache Spark - Batch Processing 2. Apache Airflow - Scheduling Workflows 3. dbt 4. Snowflake - Data Warehousing 5. Azure Databricks 6. Azure Data Factory 7. Azure Data Lake 8. Power BI - Visualization

In addition to the foundational programming languages, frameworks, and storage systems, data engineers often leverage specialized tools to streamline and enhance their workflows. Collaborative platforms like GitHub and GitLab facilitate version control and collaborative coding efforts, ensuring a systematic approach to code management. Containerization tools like Docker enable the creation and deployment of portable, scalable applications, contributing to a more agile and efficient development environment. For workflow orchestration, tools like Luigi and Oozie complement Apache Airflow, providing additional options for managing complex data pipelines.

Data engineers utilize tools such as Apache Hadoop and Spark for distributed data processing, Apache Kafka for real-time streaming, and SQL/NoSQL databases like MySQL, MongoDB for structured and unstructured data storage. ETL tools such as Apache NiFi and Talend handle data extraction and transformation, while workflow orchestration is managed by Airflow. Docker and Kubernetes facilitate containerization, and cloud services from AWS, Azure, or Google Cloud offer scalable solutions. Git ensures version control for collaboration. This comprehensive toolkit enables efficient data management and analytics, crucial for building and maintaining a robust data infrastructure.

Data engineers use a variety of tools and technologies to perform their tasks effectively. These tools span across different categories including data integration, data storage, data processing, data modeling, and more. Here are some common tools used by data engineers: 1. ETL Tools: ? Apache NiFi ? Talend ? Informatica PowerCenter ? Apache Airflow 2. Data Warehousing and Data Lakes: ? Amazon Redshift ? Google BigQuery ? Snowflake ? Apache Hadoop (HDFS) ? Apache Hive ? Apache Spark These tools enable data engineers to perform various tasks such as data extraction, transformation, loading, storage, processing, modeling, visualization, and more within their data pipelines and systems

The skills that any data engineer must have are: ?? Data governance: how data quality can be defined? ?? Data quality: how check the quality of data during the execution to avoid possible problems? ?? Data modeling: How data modeling indicate how manage data in inserting, deleting and updating operations? ?? Data pipelines: How design a solution to move data from data sources to specifc data target in security and scalable way? ?? Monitoring: How check the performances and others aspect technical of data and resources? ?? Documentation: how organize your previous and current insights to use it in the future?

1. Proficient Data Modeling and Architecture: Mastery in designing and implementing effective data models and architectures is foundational. Ensure the efficient organization and utilization of data in various scenarios. 2. Comprehensive Data Engineering Best Practices: A thorough understanding and application of data engineering best practices, including data governance, quality assurance, security protocols, testing methodologies, and meticulous documentation, are indispensable. 3. Effective Communication and Collaboration: Strong communication skills and the ability to collaborate seamlessly with diverse stakeholders, including data scientists, analysts, and business leaders, are pivotal.

The skills that are expected from a data engineer are 1. Data Modelling and Architecture 2. Database design 3. Data Integration 4. Data Profiling and Cleansing 5. Data Governance and quality checks 6. Programming language like Python to manipulate data

As a data engineer, it is essential to have good soft skills as well, other than hard technical skills. The reason for that is that you need to collaborate with a lot of stake holders to under the semantics of the data. There are going to be days where you are just sitting, and discussing that what and hows of the data. Furthermore, there are various areas other than collection, processing, and delivering the data and those are data governance, manging the quality of data, compliance with the laws such GDPR etc,

Data engineers need skills in programming languages like Python, proficiency in SQL and NoSQL databases, expertise in big data frameworks (e.g., Hadoop, Spark), ETL tools (e.g., NiFi, Talend), data modeling, and cloud platforms (AWS, Azure, Google Cloud). They should understand streaming platforms (e.g., Kafka), workflow orchestration (e.g., Airflow), and containerization tools like Docker. Collaboration tools such as Git for version control are essential, along with knowledge of data security principles. Strong problem-solving abilities are crucial for troubleshooting and maintaining robust data infrastructure.

In my opinion, data engineering is the best career who likes to play/work with big data. In this data-driven and AI world, having the right skills can lead to a sucessfull data engineering career. The most important thing is to constantly upskilling and demonstrating the data engineering knowledge by doing online courses, certifications, working on projects/initiatives and self learning.

Data engineering is currently in demand in the market and it is expected to grow further as well with the expansion of AI. All the AI algorithms eat data for the living or surviving so therefore companies will need more data engineering to collect and process the data. Having said that, the average salary is good as well. One thing I like is to see how the raw data gets converted into insights and you can see different patterns about life or how things work based on the data, which is quite fascinating to me.

A career in data engineering involves designing, building, and maintaining data infrastructure. Professionals in this field need expertise in programming languages Python, databases (SQL, NoSQL), big data frameworks (Hadoop, Spark), ETL tools (NiFi, Talend), cloud platforms (AWS, Azure), and containerization (Docker). Skills in data modeling, streaming platforms (Kafka), and workflow orchestration (Airflow) are essential. Strong problem-solving, collaboration, and data security skills contribute to creating efficient and scalable data systems, making data engineering a vital role in the data-driven landscape.

Data engineering career would in my opinion always be a rewarding career because of the concept of Big data. Big data drives a lot of Product success be it by anticipating potential market value for a product or training AI machines that help provide these product values The potential is ever growing as long as their is big data available for a product

A figura do engenheiro de dados é vital para as empresas que executam a tomada de decis?es via análise de dados, pois é responsável pela manuten??o do fluxo de dados de ponta a ponta. Para a execu??o das tarefas, é necessário que o profissional tenha como hard skills o conhecimento em linguagens de programa??o genéricas como python e scala, e orientadas a dados, como SQL; conhecimentos em sistemas de computa??o distribuídos, como Hadoop e Spark; e em bancos de dados SQL e NoSQL. Também se fazem necessárias soft skills como boa capacidade de comunica??o, a fim de interagir entre os times de arquitetura e ciência dos dados/visualiza??o de dados; capacidade de busca por informa??o; e capacidade de trabalho em equipe, com divis?o de tarefas.

An important task of a (senior) development engineer is to close the gap between design concepts and tangible, functional products. Prototyping: They contribute to the conception and design of new products, focusing on functionality and efficiency. Cross-functional teams: Effective communication with design, manufacturing and quality assurance teams is essential for smooth product development. Innovation and problem solving: They are involved in innovative problem solving to overcome challenges in product design, manufacturing processes or materials. Continuous learning: It is important to keep up to date with new technologies and industry trends in order to incorporate the latest advances into their work.

Consider the Human Element in Data Engineering: Amidst the technical intricacies of data engineering, it's crucial to acknowledge the human element. Reflect on instances where creative problem-solving and teamwork triumphed over challenges. Share anecdotes that highlight the collaborative spirit within data engineering teams, showcasing how diverse perspectives contribute to innovative solutions. Additionally, explore the evolving role of empathy and effective communication in bridging the gap between technical experts and non-technical stakeholders. Recognize that, beyond the algorithms and frameworks, the success of data engineering often hinges on fostering a culture of mutual understanding, shared goals, and continuous learning.