Gartner shed some light on this subject when it said in back in 2016 that only 15% of big data projects make it into production. We would argue that for the Data Engineering role, the same approach is necessary. A data engineer is in charge of managing the data stored and structuring it properly via database management systems. Data engineers need to have the base skills of a software engineer as well as some data specific skills. This involves a large technological infrastructure that can be architected and managed only by a diverse data specialist. Data-related skills. And so I'm gonna talk a little bit about what are the qualifications and skills that you might need in a data engineer. What I do know for sure is that the interested should pursue the foundation and don’t cancel themselves out because they decide they can’t. Data Engineer Resume. SQL. These engineers have to ensure that there is uninterrupted flow of data between servers and applications. These are the capacities that allow your enterprise to leverage the multiple, disconnected streams of data into rational, data … The more information we have, the more we can do with it. Provide data-access tools. In its core, data engineering entails designing the architecture of a data platform. So, the border between a data engineer and ETL developer is kind of blurred. Not everyone can be an engineer, however, as the demands in terms of skills and knowledge are intense. Education and Job Requirements Most aspiring engineers will need at least a bachelor’s degree from an engineering school or university, and the best-paid engineers usually have a master’s degree or Ph.D. in their field. Lastly, because of a shortage of Data Engineers and the fact that they are pretty expensive, it makes a lot of sense to look internally for software engineers, or perhaps even Data Scientists, who can bridge their skills to those of a Data Engineer role. And we engineers aren’t trained in these disciplines so on occasion it becomes “Dev Oooops”. Join the list of 9,587 subscribers and get the latest technology insights straight into your inbox. Not only will you need to have a Bachelor’s degree as mentioned earlier, but you will also need to have the right knowledge of big data technology, communicate these ideas within a team, and know how to deal with commercial IT infrastructures. Big Data Engineer Skills: Required Skills To Become A Big Data Engineer. Manage data and meta-data. There are three main functions a data infrastructure. Currently, data engineering shifts towards projects that aim at processing big data, managing data lakes, and building expansive data integration pipelines for noSQL storages. “A data engineer should have knowledge of multiple kinds of databases (SQL and NoSQL), data platforms, concepts such as MapReduce, batch and stream processing, and even some basic theory of data itself, e.g. But as a separate role, data engineers implement infrastructure for data processing, analysis, monitoring applied models, and fine-tuning algorithm calculations. Data engineer skills. The data engineering field is one that is constantly evolving, which can make a data engineer’s life more complicated. They would provide the whole team with the understanding of what data types to use, what data transformations must happen, and how it will be applied in the future. The skill set would vary, as there is a wide range of things data engineers could do. To give you an idea of what a data platform can be, and which tools are used to process data, let’s quickly outline some general architectural principles. But, the presence of a unified storage isn’t obligatory, as analysts might use other instances for transformation/storage purposes. It’s certainly possible to have most or all those data engineering skills, but it’s pretty tough to find in a single person that hasn’t been working for at least 20 years. 3 min read This article gives you an overview of the 10 key skills you need to become a better data engineer. data types, and descriptive statistics,” underlines Juan. So what can you do to find a Data Engineer, then? Along these lines, in its recent whitepaper “Data Engineering is Critical to Driving Data and Analytics Success” Gartner also recommends finding Data Engineers by hiring recent graduates and developing them internally. This entails providing the model with data stored in a warehouse or coming directly from sources, configuring data attributes, managing computing resources, setting up monitoring tools, etc.Â. So, the key tools are: As we already mentioned, the level of responsibility would vary depending on team size, project complexity, platform size, and the seniority level of an engineer. And vice versa, smaller data platforms require specialists performing more general tasks. Plainly, data scientist would take on the following tasks. If you look at the Data Science Hierarchy of Needs, you can grasp a simple idea: The more advanced technologies like machine learning or artificial intelligence are involved, the more complex and resource-heavy data platforms become. But generally, their activities can be sorted into three main areas: engineering, data science, and databases/warehouses. Or they can cooperate with the testing team. The data can be stored in a warehouse either in a structured or unstructured way. Learn the top big data engineer skills. The input provided by data scientists lays the basis for the future data platform. Other instruments like Talend, Informatica, or Redshift are popular solutions to create large distributed data storages (noSQL), cloud warehouses, or implement data into managed data platforms. However, an ETL developer is a narrower specialist rarely taking architect/tech lead roles. (Sound familiar Data Scientists?) I could go for hours on this topic but won’t. Even though at QuantHub we test for a lot of skills that apply to Data Engineers it would be difficult to develop an assessment to test for all of these skills in one go and expect one person to ace it. So what does a data engineer do? In most cases, data engineers use specific tools to design and build data storages. Ng says, "Aside from hard technical skills, a good data engineer should also have certain soft skills and qualities": Attention to detail: Data quality is extremely important when building pipelines. Everything depends on the project requirements, the goals, and the data science/platform team structure. However, if an organization requires business intelligence for analysts and other non-technical users, data engineers are responsible for setting up tools to view data, generate reports, and create visuals. But what about Data Engineers and these 14 skills they need? At a minimum a data engineer needs to write production quality code in a … An ETL developer is a specific engineering role within a data platform that mainly focuses on building and managing tools for Extract, Transform, and Load stages. Data engineers, ETL developers, and BI developers are more specific jobs that appear when data platforms gain complexity. Yes, I understand and agree to the Privacy Policy. So they would build out what are your databases, the hardware for that. Phew. Or the source can be a sensor on an aircraft body. But it also presents more job opportunities. Strong understanding of data modeling, algorithms, and data transformation techniques are the basics to work with data platforms. A data engineer found on a small team of data professionals would be responsible for every step of data flow. Matt serves as CEO at QuantHub, responsible for leading the company’s strategy, growth, and operations. Requiring custom data flows. Data engineers play a vital role for organizations by creating and maintaining pipelines and databases for injesting, transforming, and storing data. Most folks in this role got there by learning on the job, rather than following a detailed route or set of academic courses – like our friend the Database Management consultant. Implementing an Azure Data Solution. We’ll also describe how data engineers are different from other related roles. Achieving this might entail bringing together perhaps 10-30 different big data technologies. Data engineers need to be comfortable with a wide array of technologies and programming languages. All roles have essential skills, and … Linux This is mostly a technical position that combines knowledge and skills of computer science, engineering, and databases. In practice, the responsibilities can be mixed: Each organization defines the role for the specialist on its own. Engineering skills. And data science provides us with methods to make use of this data. In this case, a dedicated team of data engineers with allocated roles by infrastructure components is optimal. Big data projects. You can work as a data engineer, a senior cloud data engineer, a senior data engineer, and a big data engineer, among other roles. That really is a dismal result for all the effort going into big data. Some of the responsibilities of a data engineer include improving data foundational procedures, integrating new data management technologies and softwares into the existing system, building data collection pipelines, among various other things. Then I realized that like others it’s taken 20 years to acquire, hundreds of data sets, close to a hundred companies and thousands of hours training others and problem solving with data. A data engineer conceives, builds and maintains the data infrastructure that holds your enterprise’s advanced analytics capacities together.. Big Data engineering is a specialisation wherein professionals work with Big Data and it requires developing, maintaining, testing, and evaluating big data solutions. The skill set would vary, as there is a wide range of things data engineers could do. They might do things like build infrastructure. Data related expertise. As with Data Scientists, our recommendation would be to decide which specific skill sets you need and build a portfolio of talent with those skills. Historically, the data engineer had a role responsible for using SQL databases to construct data storages. Data engineers will be in charge of building ETL (data extraction, transformation, and loading), storages, and analytical tools. A data engineer needs specific technical skills. Managing this layer of the ecosystem would be the focus of a pipeline-centric data engineer. Data engineers job descriptions vary significantly as they are asked to work on many different projects. So, starting from configuring data sources to integrating analytical tools — all these systems would be architected, built, and managed by a general-role data engineer. Our friend the software developer of 20 years recommended a team of three: a highly skilled coder with an understanding of data science functions, business expert / business analyst, and a statistics expert. We need to store extracted data somewhere. All of this has reminded me of the sometimes-overlooked importance of the Data Engineer’s role. If your engineers are doing non-solution development work – Dev Stops. Data engineering is a part of data science, a broad term that encompasses many fields of knowledge related to working with data. With an incredible 2.5 quintillion bytes of data generated daily, data scientists are busier than ever. These tasks typically go to an ETL developer. Data scientists are the basis for most data-related projects. For a data engineer, a bachelor's degree in engineering, computer science, physics, or applied mathematics is sufficient. Nevertheless, getting the right kind of degree will help. That IS a lot of skills (and sub-skills)! Extracting data: The information is located somewhere, so first we have to extract it. The problem is, there is currently no coherent or formal education or career path available for Data Engineers. So, experience with the existing ETL and BI solutions is a must. These are constantly subject to change, so one of the most important skills that a data engineer possesses is the underlying knowledge for when to employ which language and why. Or the data may come from public sources available online. Matt has a passion for developing authentic relationships with customers to truly understand what drives them, and then crafting creative solutions to their most critical problems. Because Data Science seems to be the immediate need that everyone is seeking to fill en masse in the race to deploy AI solutions. During the development phase, data engineers would test the reliability and performance of each part of a system. Hire multiple people to complete the portfolio of data engineering skill sets. We ranked the top skills based on the percentage of Data Engineer resumes they appeared on. Why this focus? This field is for validation purposes and should be left unchanged. So, theoretically the roles are clearly distinguishable. Data Engineer is the fastest growing job title according to a 2019 analysis. A data engineer in this case is much more suitable than any other role in the data domain. Regarding that overall Data Engineer skill set required, the ability to create a data pipeline is one thing. If you’ve been wondering, “what does a Data Engineer do?”  This is the job of a Data Engineer! Need immediate assistance? So, there may be multiple data engineers, and some of them may solely focus on architecting a warehouse. Track pipeline stability. Development of data related instruments/instances. So, while you search for the definition of “quintillion,” Google is probably learning that you have this knowledge gap. Pre-employment tests – Do They Help Avoid False Positives. But generally, their activities can be sorted into three main areas: engineering, data science, and databases/warehouses. While at Daxko, Matt led the team to deliver the first machine learning/AI solution to the market, predicting customer membership churn and also propensity to donate. Regarding that overall Data Engineer skill set required, the ability to create a data pipeline is one thing. Companies generate a large amount of data from different sources and the task of a Data Engineer is to organize the collection of data information, it’s processing and storage. Total price includes each user quantity within the tier. 1. Both those in the Data Engineering profession and those trying to hire Data Engineers have a tough job. developing reporting tools and data access tools. Data science is an emerging field, and those with the right data scientist skills are doing. Support Chat is available to registered users Monday thru Friday, 8:00am to 5:30pm. And one software developer who commented in reaction to the Data Engineer skills slide also offered living proof of this when he said, “I can cover almost all of the roles at various levels, but it’s taken 20 years and without a team even with all of that ability a single person isn’t going to produce magic.”, And another development manager seconded, “Yeah, only so many hours in a day.”. Big Data Engineer Skills and Responsibilities. I can’t lie, at QuantHub we share the same obsession with all things Data Science. Data engineers are responsible for deploying those into production environments. Netflix follows the “one for one rule” – it has as many Data Engineers as Data Scientists, and Data Engineers are equally important. Building a streaming data pipeline (rather than batch based) is yet another new set of skills that Data Engineers must implement. Data pipeline maintenance/testing. As evidenced by these 14 skill sets, Data Engineers brings a lot to the table in terms of capabilities that impact the outcomes of data science and analytics efforts across the organization. Big Data Frameworks/Hadoop-based technologies: With the rise of Big Data in the early 21 st century, a new framework was born. You can be a solid addition to any team if you build the right foundation.” – Data Management consultant, “Oh my — you’ve hit a nerve! It’s another thing to be able to create a system that allows an organization to rapidly deploy data pipelines, monitor them and ensure fault tolerance of the entire system, all in a cost-effective manner that satisfies end user needs and business goals. So, along with data scientists who create algorithms, there are data engineers, the architects of data platforms. As the complexity grows, you may need dedicated specialists for each part of the data flow. At QuantHub we test for Data Engineering skills in addition to Data Science skills because we recognize that both roles are needed to get the job done. Here are the skills I see as most critical for success as a data engineer. Skills needed to become a Data Engineer. And the more complex a data platform is, the more granular the distribution of roles becomes. For instance, the organizations in the early stages of their data initiative may have a single data scientist who takes charge of data exploration, modeling, and infrastructure. Recently though, I was at a large Data and Analytics conference and a speaker threw up a slide similar to the image above to demonstrate the many data engineering skills needed to do the job of a data engineer successfully. Extract, Transform, Load is just one of the main principles applied mostly to automated BI platforms. The data can be further applied to provide value for machine learning, data stream analysis, business intelligence, or any other type of analytics. Transformations aim at cleaning, structuring, and formatting the data sets to make data consumable for processing or analysis. In some organizations, the roles related to data science and engineering may be much more granular and detailed. Prior to joining QuantHub, Matt spent the last 15 years running product and tech at PE-backed companies, including building a product and engineering organization at Daxko to deliver 10x revenue growth, 7 acquisitions, and 3 enormously successful recapitalizations in just 10 years. In terms of corporate data, the source can be some database, a website’s user interactions, an internal ERP/CRM system, etc. The language is often thought of as the “graduated” version of Excel; it is able to handle large datasets that Excel simply can’t. (As I heard someone call it — “Dev STOPS not Dev Ops”). Additional storage may contain meta-data (exploratory data about data). Is it my imagination or did we overlook the fact that Engineers are now responsible for deployments, monitoring, and even environment configuration. Depending on the project, they can focus on a specific part of the system or be an architect making strategic decisions. Regardless of the focus on a specific part of a system, data engineers have similar responsibilities. So, the number of instances that are in between the sources and data access tools is what defines the data pipeline architecture. Skills required to be a data engineer You will need the following skills for this role, although the level of expertise for each will vary, depending on the role level. Machine learning models are designed by data scientists. Over 9 years of diverse experience in Information Technology field, includes Development, and Implementation of various applications in big data and Mainframe environments. Skills for any specialist correlate with the responsibilities they’re in charge of. While data science and data scientists in particular are concerned with exploring data, finding insights in it, and building machine learning algorithms, data engineering cares about making these algorithms work on a production infrastructure and creating data pipelines in general. In some cases, such tools are not required, as warehouse types like data-lakes can be used by data scientists to pull data right from storage. Enter the total number of employees to be screened annually. Database-centricLet’s go through each one of these categories. A brief overview of some of the skills on the slide tells a little bit about the complexities of a Data Engineering job: Phew. Communication skills (data) . The automated parts of a pipeline should also be monitored and modified since data/models/requirements can change. One of the most sought-after skills in dat… The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. Skills for any specialist correlate with the responsibilities they’re in charge of. To find a Data Engineer, you need to find someone who has developed a boatload of skills across a wide variety of disciplines – even more than the Data Engineering skills slide entails. 14 Data Engineer skills on the slide, several of which implied that even more underlying skills were needed, I was reminded that our focus is often on communicating with customers about the combination of diverse skills needed to fill a Data Scientist role. According to Glassdoor, the average salary for a data engineer is $137,776 per year, with a reported salary range of $110,000 to $155,000 depending on skills, experience and location. At its core, data science is all about getting data for analysis to produce meaningful and useful insights. Database/warehouse. Let's take a look at four ways people develop data engineering skills: 1) University Degrees. Scaling your data science team. Instructor-led courses to gain the skills needed to become certified. If you are considering becoming a data security engineer, it will be helpful to know what skills are specifically useful in both landing the job and ensuring that you achieve your goals within the job once you have got it. Objective : Experienced, result-oriented, resourceful and problem solving Data engineer with leadership skills.Adapt and met challenges of tight release dates. One of the various architectural approaches to data pipelines. Data engineers would closely work with data scientists. Staring up at the (gasp!) Or they can use no storage at all. skills needed to fill a Data Scientist role, the work of the data engineer aligning very well with the strategy of the business, only 15% of big data projects make it into production, advocated for an approach to building Data Science capabilities, Data Engineering is Critical to Driving Data and Analytics Success, hire graduates and entry level employees with a long term view towards developing them, The Role of Data Analysts in 2020 and Beyond, A Data Driven Organization: How to Build it in 3 Essential Steps, Building Data Science Teams Means Playing the Long Game, Retrain Employees for the Age of Data Science and AI. So, we might as well learn from the world of Data Science and start building Data Engineering teams using some of the methods we see happening in that field – hire graduates and entry level employees with a long term view towards developing them into Data Engineers, hire from within where possible, and hire a team (rather than a person) that fills out the portfolio of Data Engineering skills your organization needs. The role requires a complex combination of tasks into one single role. Although data engineers need to have the skills listed above, the day to day of a data engineer will vary depending on the type of company they work for. You can use a test like QuantHub to assess strengths and weaknesses and then provide training, tools, and mentoring they need to be able to fill the role of Data Engineer. These tools can either just load information from one place to another or carry more specific tasks. Are these not just as rare and diverse a set of unicorn-like skills? Most tools and systems for data analysis/big data are written in Java (Hadoop, Apache Hive) and Scala (Kafka, Apache Spark). The bigger the project, and the more team members there are — the clearer responsibility division would be. Pipeline-centric 3. All downstream work is only as good as the quality and integrity of the data … In practice, a company might leverage different types of storages and processes for multiple data types. Processing data systematically requires a dedicated ecosystem known as a data pipeline: a set of technologies that form a specific environment where data is obtained, stored, processed, and queried. If you are struggling to get started on what to learn, start with the first topic and proceed through the list. Essential Skills for Data Analysts 1. +1 888 208-6840. A business intelligence developer is a specific engineering role that exists within a business intelligence project. Python along with Rlang are widely used in data projects due to their popularity and syntactical clarity. 2 Users, 200 Candidates Screened Annually, $589/mo, 12-Month Agreement, 1 User, 50 Candidates Screened Annually, $239/mo, 12-Month Agreement. The problem of finding people who possess these multiple skill sets will just get worse. Warehouse-centric. One of the key members of a data science team is a data engineer. The role of data engineer needs strong data warehouse skills with a thorough knowledge of data extraction, transformation, loading (ETL) processes and Data Pipeline construction. However, if your data workflow is not efficient, the end results in terms of the lack of data science effectiveness and efficiency as well as Data Scientist frustration and turnover will cost you more. Industry analysts often suggest that GCP is the best product for data engineering. Data scientists are usually employed to deal with all types of data platforms across various organizations. Yet, there are categories of skills that are consistently desired in a data engineer and serve as a foundation for learning new technologies. The right data engineer skills section will do two things: show that you have the fundamental data management skills down pat and that you will be able to learn a new tech stack quickly. Skill set of a data engineer broken by domain areas. Hopefully this piece has illuminated the specific talents, skills, and requirements expected of a Big Data Engineer. The responsibilities of a data engineer can correspond to the whole system at once or each of its parts individually. Business intelligence (BI) is a subcategory of data science that focuses on applying data analytics to historical data for business use. These are the specialists knowing the what, why, and how of your data questions. Data storing/transition: The main architectural point in any data pipeline is storages. Below we've compiled a list of the most important skills for a Data Engineer. The role of a data engineer is as versatile as the project requires them to be. And to be a Data Engineer, you must embody that unicorn. The importance of the Data Engineer role was accurately reflected in the words of one Netflix Data Scientist who stated:  Good data engineering lets Data Scientists scale. For instance, you might form a team of a data product manager/owner, a Data Scientist, and a Data Engineer and “cross pollinate” skill sets. According to the Bureau of Labor Statistics, career opportunities in this field are anticipated to grow 19% by 2026, much faster than average. I’ll get off the soapbox now…”  – BI and Technical PM. In data engineering, the concept of a, Transformation: Raw data may not make much sense to the end users, because it’s hard to analyze in such form. Which tech skills are most in-demand for data engineers? However, to become a Data Engineer, you need to have some excellent skills like Databases, Big data, ETL & Data Warehousing, Cloud computing as well programming languages. In a recent post, we advocated for an approach to building Data Science capabilities that encouraged a move away from expecting a single “unicorn” (or even two unicorns) to have all the necessary skills to do the job, to a more “portfolio”- based approach to developing Data Science capabilities. SQL, or Structured Query Language, is the ubiquitous industry-standard database language and is possibly the most important skill for data analysts to know. Machine learning algorithm deployment. Top Data Engineer Skills. Yikes. So, a data engineer is an engineering role within a data science team or any data related project that requires creating and managing technological infrastructure of a data platform. A data engineer is a technical person who’s in charge of architecting, building, testing, and maintaining the data platform as a whole. There are specific responsibilities that are expected of a big data engineer. Let’s have a look at the key ones and try to define the differences between them. I’ve got plenty of examples of the wrong person making the wrong decision resulting in increased costs or even risk of data exposure. For example, they may include data staging areas, where data arrives prior to transformation. Broadly, you can classify data engineers into a few categories: 1. Classical architecture of a data pipeline revolves around its central point, a warehouse. In this article we’ll explain what a data engineer is, their scope of responsibilities, skill sets, and general role description. It will correlate with the overall complexity of a data platform. Architecture design. Hiring practices that focus on finding a single person that can basically cover all roles are limiting because the pool of candidates will be such a small number that hiring will take forever, if you can even find the “right” person at all. 12-Month Agreement. While a data engineer and ETL developer work with the inner infrastructure, a BI developer is in charge of. I find the statistics is often the missing spoke, but with a good foundation, the right person can develop this.”  –  Analytics recruiting consultant, “I actually felt pretty great about myself with this diagram which is unusual for me. While there must be numerous reasons for this low success rate, one explanation to this statistic is that companies are so focused on getting to the insights from data science tools, that they fail to put in place the data pipelines and workflows that can allow data to be useful to the business on an ongoing basis, according to service level agreements and within a necessary time frame to make it valuable. That IS a lot of skills (and sub-skills)! Moving ahead in this Big Data Engineer skills blog, let’s look at the required skills that will get you hired as a Big Data Engineer. Some would argue that this portfolio approach would be more expensive. There are several scenarios when you might need a data engineer. When I put this slide out to some folks on LinkedIn and asked if a Data Engineer can meet all of these skill requirements, here are some comments I received from industry professionals: “Ah – the search for the unicorn! While the field is rapidly growing, it is fraught with obstacles - therefore, attaining the best education possible while filling any gaps in skill sets with proper certification is key. If the project is connected with machine learning and artificial intelligence, data engineers must have experience with ML libraries and frameworks (TensorFlow, Spark, PyTorch, mlpack). Even for medium-sized corporate platforms, there may be the need for custom data engineering. As a data engineer is a developer role in the first place, these specialists use programming skills to develop, customize and manage integration tools, databases, warehouses, and analytical systems. Data Security Engineer Skills. A University education isn't necessary to become a data engineer. Data Engineer with Python In this track, you’ll discover how to build an effective data architecture, streamline data processing, and maintain large-scale data systems. This is still true today, but warehouses themselves became much more diverse. For example, 8.5% of Data Engineer resumes contained Python as a skill. Generalist 2. In the Big Data industry we spend an enormous amount of time and effort deciphering the role of Data Scientists, drawing Data Science unicorns (figuratively) and discussing to the nth degree the relative importance of programming vs. problem solving skills in candidates. More specific expertise is required to take part in big data projects that utilize dedicated instruments like Kafka or Hadoop. The Data Engineer will also be required to draft regular performance and progress reports and prepare presentation for senior data engineering management and senior data science leadership, reports that have to be clear, concise, engaging, and convincing, which will require exceptional communication skills to deliver. With the ever increasing volumes of enterprise data and new technologies appearing all the time, Data Engineers have become vital members of any analytics team. A data engineer is responsible for building and maintaining the data architecture of a data science project. But, understanding and interpreting data is just the final stage of a long journey, as the information goes from its raw format to fancy analytical boards. The warehouse-centric data engineers may also cover different types of storages (noSQL, SQL), tools to work with big data (Hadoop, Kafka), and integration tools to connect sources or other databases. General-role. Big data engineers need to have a combination of programming and database skills to be successful. These storages can be applied to store structured/unstructured data for analysis or plug into a dedicated analytical interface. We’ll go from the big picture to details. In the case of a small team, engineers and scientists are often the same people. Again, that’s a lot of skills! In most cases, these are relational databases, so SQL is the main thing every data engineer should know for DB/queries. The growing complexity of data engineering compared to the oil industry infrastructure. Data specialists compared: data scientist vs data engineer vs ETL developer vs BI developer, 10 Ways Machine Learning and AI Revolutionizes Medicine and Pharma, AI and Machine Learning in Finance: Use Cases in Banking, Insurance, Investment, and CX, 11 Most Effective Data Analytics Tools For 2020. Monitoring the overall performance and stability of the system is really important as long as the warehouse needs to be cleaned from time to time. Here’s a general recommendation: When your team of data specialists reaches the point when there is nobody to carry technical infrastructure, a data engineer might be a good choice in terms of a general specialist. In this form, it can finally be taken for further processing or queried from the, Strong understanding of data science concepts, Set standards for data transformation/processing, Define processes for monitoring and analysis. Pipeline-centric data engineers would take care of data integration tools that connect sources to a data warehouse. High-performant languages like C/C# and Golang are also popular among data engineers, especially for training and implementing ML models.