What data engineering career paths have you explored?

I feel newbies/starters even experienced get it wrong when they assume data engineering is just about building pipelines and making sure ETL & ELT processes in between need to be taken care but the reality is very different and complex. Data engineering starts with knowing/building/following industry standards wrt data warehouses, data marts & following strict data-modeling rules to ascertain & minimize future risk when integrating new data sources. Data integration nuisance between varied systems must. Sql, Git, cronjob, airflow, advance bash script programming are table stakes.

Data engineering roles can primarily exist in two different frameworks: data warehousing and data lake environments. ETL/ELT and developing pipelines for data extraction are common in both scenarios. It is important to understand the similarities and dissimilarities in both environments, theories, best practices as well as the tools and technologies involved. Data engineer, ETL developer, Big data engineer, Data warehouse engineer and Data Artchitect are few of roles related to data engineering. These roles encompass different aspects of managing and processing data within various environments, such as data warehouses, big data platforms, and overall data architecture.

Word engineering is to design and build. So semantically data engineering is supposed to mean designing and building data. Having said that I believe the market and industry for data is still in it's nascent stages unlike software engineering. So the understanding of what a data engineer do or what is data engineering, is still highly subjective to an engineer and/or the organization depending on the problem they are solving. Unlike software engineering which has functionality as its core value proposition, data engineering offers information/truth. Thus, engineering aspect for achieving information/truth can be viewed based on tech-stack, business value, skills, etc. In summary, there's no well-defined framework as of now. Keep learning !

With experience in data engineering, exploring the business requirements and advancing to enable data-driven decision making is equally important. Emphasising on data accessibility, availability, scalability is crucial, also ensuring the critical role in enabling data-driven decision making by providing clean, transformed, and aggregated data, optimizing data processing workflows, and collaborating with data scientists and analysts makes data engineers more industry driven. Having ground concepts of programming, data cleaning, querying, security and various frameworks related to Hadoop, spark, kafka, cloud and more, data engineers focus on processing and analyzing large volumes of data using distributed computing frameworks.

Data engineering is much more closer to software engineering than it is to data analysis and data visualisation areas. A data engineer will work much closer with databases, much more closely with virtual machines and network. They will likely need to work with orchestration layers which will require them to understand operating system concepts, networking and distributed computing. Data engineering is critical as it enables data analysts and data scientists to build their own insights and implementations.

In contrast to the data engineer, soft skills are of particular importance as a data analyst (this in no way means that the technical skills are not also important!). As a data analyst, it is very important to present and explain the findings in the right way, so just finding the right results is only half the story. You have to be able to present them in the right way, otherwise stakeholders might not be able to follow your advice and may make the wrong decisions.

A data analyst plays a pivotal role in helping organizations derive actionable insights from their data assets. The primary focus of a data analyst is to transform raw data into valuable information that can drive business growth, efficiency, and decision-making. Some aspects: - Data analysts are responsible for exploring and dissecting data sets, identifying trends, patterns, and anomalies. - Data analysts formulate hypotheses and conduct statistical tests to validate or reject these hypotheses. - Data analysts are skilled in creating visual representations of data, such as charts, graphs, and dashboards. - Based on their data analysis, data analysts provide actionable recommendations to improve business processes.

In many organisations I have seen that Data Engineer is also doing the job of a Data Analyst, bit weird, but it's true. Nowadays, single people can handle 2 tasks efficiently, also a Data engineer who loaded the data into a warehouse knows data better than others , if he/she knows dashboarding then he/she can be a data analyst also.

As I data analyst. I have utilized DOMO to query the data as needed. I have worked on ETLs, dashboards and cards to deliver the KPIs most needed. Further, have utilized SQL. The end goal of providing the insights most desired in the best manner.

A data scientist combines expertise in data analysis, statistics, machine learning, and domain knowledge to solve complex business problems and generate actionable insights. Some key areas: - start by exploring and cleaning data to ensure its quality and suitability for analysis. - employ statistical techniques to identify patterns, trends, and correlations in the data. - build predictive and prescriptive models using machine learning algorithms. - develop custom algorithms and models when off-the-shelf solutions are insufficient for solving specific business problems.

Data passes through Data Engineer as they make raw data readable by transforming and loading them into warehouses, then reach to Data scientists who will apply ML/DL algorithms to give you trend/insights.

The role of a Data Architect is centered around designing and managing an organization's data infrastructure and architecture to ensure that data is stored, organized, and accessible in a way that meets business needs, supports data analysis, and aligns with long-term strategic goals. Data architects play a crucial role in establishing the framework for effective data management within an organization.

Data Architect comes into play at very initial time when business requirement discussion ends and development about to start, because data architect is the one who plans how the data will flow from source to final dump, they do data modelling ensuring backups, robustness, efficiency, decides which platform, services, tools should be used in order to provide a optimal solution.

The role of a DataOps Engineer is relatively new but increasingly important in the field of data management. DataOps is a set of practices and principles aimed at improving the collaboration and efficiency of data-related processes within an organization. DataOps Engineers are responsible for implementing and maintaining these practices to ensure that data pipelines, workflows, and systems are streamlined, automated, and operate smoothly.

- While data engineers focus on building and maintaining data pipelines, DataOps engineers prioritize improving these pipelines' efficiency and reliability through automation and CI/CD. DataOps emphasizes a more agile approach, integrating development, operations, and quality assurance. - Essential skillsets for DataOps are in automation tools, containerization (Docker, Kubernetes), and cloud services (AWS, etc.). Familiarity with infrastructure as code, version control (Git), and monitoring tools is also critical. - Start by contributing to the automation and optimization of existing pipelines in your current role. Seek opportunities for cross-functional collaboration to understand the operational aspects of data workflows.

A Data Visualization Engineer is a professional who specializes in creating and designing data visualizations to present complex data in a clear and understandable manner. They play a crucial role in data analysis and communication by using visual elements such as charts, graphs, maps, and dashboards to convey insights from data to a wide range of audiences, including business stakeholders, decision-makers, and the general public.

The role towards data engineer is more focus on understanding data, the categorization could even be more vast, for eg: - Data Ingestion and Integration Engineer - Data Pipeline Engineer - Data Warehouse Engineer - Bigdata/cloud engineer Major responsibilities for any data engineer could revolve around, Designing and implementing data architectures and infrastructure, Building and maintaining data pipelines for data ingestion, transformation, and storage, ensuring data quality, reliability, and security. Collaboration and communication with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions. Optimizing data processes for performance, scalability, and cost-efficiency.

Roles in data often may have a lot of overlap with each other. A data engineer may have to build a data visualisation dashboard and may be asked to understand and build metrics. Data scientists often most of their time cleaning and prepping their data as good data is the backbone of any effective model. In this regard, their work has a huge overlap with data analysts. One of the ways to be a proficient data individual is to slowly develop a good understanding of the entire data lifecycle and how each of the layers work together to produce a final data product.

I started working in the Automation domain 6-7 years ago and was doing orchestration, collecting the data, processing the data and presenting the data, visualisations using task based workflows which are similar to pipelines nowadays. In those data Engineering term was not very well known but the tasks were very same to now days data Engineering. Collecting & processing data from numerous sources with variety of data types and huge in quantity are very common tasks in Data Engineering. In simple words making the data available for analytics, data science team and other analysts with automated and standardized pipelines is Data Engineering. It could be cloud based or on-prem version of tools depending upon the requirements.

Tenho visto um crescimento, ao menos em divulga??o, do uso do Data Build Tool (dbt) nos projetos de engenharia de dados. Trabalhei com dbt por 8 meses e achei a proposta bem interessante, porém para manter a produtividade em alta, ainda considero a as ferramentas point-and-click mais adequadas e práticas.