How to Become a Data Engineer: Skills, Certifications, Roles and Responsibilities

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When Clive Humby, a renowned British mathematician and data science entrepreneur, proclaimed data to be the new oil, businesses realized that data is not a mere byproduct but the core of their operations.  

From that moment on, companies started looking at data as a strategic asset and began their search for people to help them manage immense loads of data. And not just scientists, but engineers who can retrieve data from various sources. They are urgently needed across industries - information technology and services, computer software, telecommunication, financial services, healthcare, etc. 

As a result, we are seeing a growing need for this new talent group. Just between 2013 and 2015, the number of data engineers more than doubled and, to this day, has the highest year-over-year growth of all jobs in the industry

Data engineering and cloud technologies are the future of extracting value from massive amounts of existing and new data. They are the bridge between business and data.

Almost half of all data engineers come from a software engineering background due to their computer programming skills. But software engineers should tread carefully when stepping into the world of data engineering. While they are capable of writing elaborate codes and algorithms, they will need to take a much more holistic approach to data development. They also need to learn to integrate machine learning (ML) algorithms into the apps they are building and how to shape their programming style to meet different requirements.

What is a data engineer?

A data engineer is responsible for building systems to collect, manage, and convert raw data into comprehensible information for enterprise-grade applications. Utilizing ETL pipelines, data engineers make the data more readable to others in their organization, like data engineers and business analysts. 

Key responsibilities of a data engineer

Data engineering is a relatively young field, which is why the responsibilities are not as clear-cut. Ultimately, the specific role and expectations of data engineers will differ based on the company and project in question. 

This ‘uncertainty’ is, in fact, what attracted one of our colleagues to the world of data engineering. As he put it:

There are several functions every data engineer is expected to know (or be prepared to learn) how to perform. Here’s what might be expected of you:

  • Building and managing ETL/ELT data pipelines, which includes scheduling, execution, monitoring, and managing metadata. 
ETL vs ELT Data Pipelines Differences
  • Managing different data sources based on unique business requirements. 
  • Extracting data from the source system, including databases, static files, external API, cloud storage, etc. 
  • Transforming data by mapping, filtering, denormalizing, aggregating, enrichment, etc. 
  • Loading data into the destination system, like a data warehouse, cloud storage file system, cache database, etc.
  • Managing data warehouse, which can involve modeling data for analytical queries, securing data quality, and ensuring optimal warehouse performance. 
  • Serving data to end-users, in other words, setting up a dashboard tool, granting permissions, setting up data endpoints, etc.
  • Building systems for data storage, collection, accessibility, analytics, and quality algorithms.
  • Collaborating with data scientists to build necessary data infrastructures.
  • Building a data strategy to define which data to collect and how, where to store it, how to build the architecture that will meet the needs, educating users on efficient data usage, and setting up data sharing permissions. 
  • Deploying ML models, setting up learning pipelines, monitoring, and logging systems. 

What skills do you need to become a data engineer?

Wondering if you are right for the part? We have to be honest here – data engineering is no child’s play. 

Data engineers need to possess data-driven decision-making capabilities. They are also expected to display a solid understanding of mathematics and programming languages, that is, the basics of computer sciences. That is why you’ll commonly see employers looking for someone who knows how to handle SQL, Python, Spark, Snowflake, Kafka, and Linux, and preferably has good knowledge of a cloud provider like Azure/AWS. 

I love how data engineering allows you to widen your horizons.
For me, it’s an opportunity to tap into my existing skills and see how these things I already know relate to new problems we encounter in the data and cloud space. 

Technical requirements

If you have already skimmed through a few data engineering job postings, you probably noticed a few unique lines of requirements in each one. As we mentioned earlier, specific expectations are impossible to predict, but we don’t want you blindsided, so we will be a bit more detailed here. From our experience, data engineers are usually expected to master:

  • Structured Query Language or SQL Systems, because data engineering requires tools that enable interaction with database management systems and data analysis. Data engineers must be familiar with basic CRUD, SQL internals, and data modeling concepts and schemas.
  • Programming/scripting languages, as either Python or Java are used daily to perform core activities, like data analysis. Knowledge of a scripting language is useful for automating multiple data-processing activities.
  • Linux system, since it is popular for application development. It’s key to familiarize yourself with file system commands, commands necessary for data processing and retrieving metadata, as well as bash scripting concepts like looping, control flow, and passing input parameters.
  • Cloud Computing. With the majority of companies moving their data to the cloud, data engineers need to learn how to utilize some of the biggest cloud computing service platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
  • Kafka and how distributed systems work. Familiarize yourself with different types of joins across data sets, common data processing techniques, patterns, and concepts, and also learn to optimize data processing code to use all the cores and memory available in the cluster. 
  • Spark, Hive, and other tools required for the efficient handling of large datasets.
Data Engineering Book Recommendation
  • Tools used to build ETL/ELT pipelines, such as Snowflake and Star, used for working on cloud data warehouses. In time, you should also note the most common pitfalls and how to avoid them.
  • OLAP database, how it works, data modeling concepts, facts, and dimensions. Learn to figure out client data to design a database to match the requirements.
  • Steam processing and queuing systems, when and how to use them.

How to tackle your first data project

Basic terminology (aka, data engineering buzzwords):
upstream process is any task that precedes your work
downstream process is any task executed after you finish your work

Entering the world of data engineering can be daunting, even for experienced software engineers who fulfill all technical requirements. Before you start, it’s crucial to understand the data infrastructure, which requires identifying and noting key components and interfaces. 

An interface refers to how the components of the data infrastructure communicate. Interfaces can include libraries and REST APIs. 

Components are unique for every project, but in most cases, they include:

  • Application database
  • Data warehouse
  • Data visualization tool
  • Data pipeline orchestration engine
  • Execution engine
  • Event streaming system
  • Cloud storage
  • Distributed processing system
  • 3rd party connectors

Working on an existing pipeline

As soon as you join the project, seek to understand the current state of the pipeline. When asked to modify the existing pipeline, be aware of any downstream tasks to avoid breaking them. It’s also essential to communicate the changes to the end user to avoid irreversible issues. 

Our advice, in this case, would be to try to use the same pattern unless you come up with an approach that will provide significant gains in terms of complexity, time, and cost reduction. 

Building a new pipeline

When working on a new pipeline, compare it to the most similar one. Take the time to create a design document, and don’t hesitate to consult with people on the team to get approval and advice on how to best modify the approach you had in mind. 

Always ask yourself why you are doing this task, what impact it will have on the business, data team, developers, or any other relevant parties, and whether it can be achieved with existing data/code. A thorough understanding of the assignment helps you devise a strategy to tackle any challenges and come up with the best ways to perform a given task.

Big data and the healthcare industry

Big data informs planning and decision-making processes across industries. From banking and financial services, cybersecurity, education, advertising, and marketing to transportation and healthcare - big data has become an integral part of every aspect of our lives. 

We here at Inviggo are particularly interested in its application in the healthcare industry since it yielded a ton of positive, life-altering outcomes.

The past decade brought us a surge in wearables and sensors designed to collect real-time data to include in electronic health records. Industry professionals now leverage big data to make significant advancements in telemedicine and telehealth, delivering effective medical assessment, consultation, diagnosis, and supervision.

Big data facilitates early detection, real-time alerting, prediction, and prevention of serious conditions. On a grander scale, it improved strategic planning, medical research, analysis of medical images, and prediction of epidemic outbreaks, thus minimizing their effects. All this helps reduce the costs of treatment and improve the quality of life in general. 

It is also beneficial for simpler and more accurate staff scheduling, patient management, recordkeeping, and reduced error rate - all in an effort to deliver the most effective medical services that will enhance patient care. 

So what is the role of a data engineer in the healthcare industry?

Data engineers manage and decipher complex data to ensure that healthcare professionals have the information necessary to improve the quality of their work. In addition to the human perspective, there’s the need to conform to compliance and audibility requirements. That is why this industry usually needs someone with classic computer science skills who is comfortable with the level of complexity. 

But those who are thinking about testing their data engineering skills in the healthcare arena should be aware of a few challenges the industry’s still facing:

  • Managing multiple data sources governed by different hospitals, administrative departments, and states;
  • Data sharing and reporting, as the healthcare industry is still in the process of moving on from the standard regression-based methods to predictive and graph analytics or machine learning;
  • Ensuring absolute protection and security of confidential patient information that comes from different sources.

Finally, because handling data in the healthcare industry also involves many processes using multiple tools, data engineers are expected to be highly literate.