In today's data-driven world, data platform teams are an integral part of businesses of all sizes. These teams provide an excellent opportunity for data engineers to improve their skills and specialize in specific domains of data that are crucial to business operations, such as customer data or product/behavioral data.
Data engineers can choose to specialize in specific capabilities of the data platform, such as reliability engineering, business intelligence, experimentation, or feature engineering. These roles offer a broader, albeit shallower, understanding of each business use case, making it an easier transition for those coming from a software engineering background. This versatility allows data engineers to adapt and contribute to various aspects of the data platform, further enhancing their skill set and expertise.
"I look for candidates who have a proven track record of making an impact and hitting the ground running, either in their primary occupation or by contributing to open source projects," Shane Murray, CTO of Monte Carlo Data, told kanger.dev. Data engineering, he shares, is constantly changing, and those who do not like change may not be suited for the field.
It is imperative, therefore, that data engineers keep up with the evolving landscape brought about by artificial general intelligence.
AI Impact and Opportunities
The demand for data-driven decision making will be further increased by the breakthrough of AI advancements, leading to the expectation of continued growth in career prospects for data engineering. Although AI may automate certain aspects of data engineering, the need for human expertise in areas like data modeling, data integration, and data quality assurance is unlikely to be replaced.
"AI at the end of the day is an accelerator; GPT-4 enables me to finish tasks in hours that previously took weeks," remarked Deexith Reddy, an experienced data engineer and open source enthusiast, during our interview call. "Generative AI may reduce demand for data engineers in general coding but will create more research and critical thinking opportunities."
The modern data stack—comprising cloud-based solutions for data ingestion, transformation, orchestration, visualization, and data observability—is rapidly gaining prominence in data engineering. For a rewarding career, "one must focus on both the breadth of data analytics and the depth of data engineering," said Reddy. "By comprehending the end-to-end problem, from data source to analytical use case, data engineers can become invaluable assets to their teams and the overall business."
As businesses increasingly treat data as a product, the demand for skilled data engineers will continue to rise. However, the landscape is changing due to emerging requirements, and the field is becoming exceedingly competitive. The critical data engineering skills, therefore, involve not only building reliable and scalable data products but also applying product thinking to drive the vision, roadmap, and adoption of these products.
Whether you are an experienced or aspiring data engineer witnessing the surge of AI advancements, you will need a strong grasp of logic and a solid understanding of the fundamentals.
Here's a concise overview of the foundational skills, in contrast to the advanced skills we'll soon update in the article:
Data engineers will require exceptional skills to write code that is both secure and clean, with the most commonly used programming languages being Python, Scala, Java, and SQL. However, some companies may require you to know other languages like Golang, R, or Ruby.
While AI will make learning to code easier, it will not subtract the requirement for advanced programming skills. Generative AI won't eliminate the need for abstract thinking, logical thinking, problem-solving, and analytical skills. Instead, it will heighten the competitive landscape for data engineers to improve their programming skills, despite writing less code.
Data engineers should possess an exhaustive knowledge of database management, with strong functional expertise in both relational and non-relational databases.
Proficiency in both SQL and NoSQL is essential for data engineering roles, as well as expertise in various cloud-based tools and platforms, including AWS Redshift, MongoDB, AWS S3, Cassandra, and GCP BigQuery.
Cloud computing continues to be the primary platform for most data engineering tasks. The use of cloud-based services has increased significantly over the years, with AWS, Google Cloud, and Azure being the most popular platforms for deploying and managing data engineering solutions.
You can learn cloud data engineering skills without going into debt by pursuing a high-ROI open-source cloud engineering bootcamp offered by the Linux Foundation or by pursuing AWS Data Analytics, Google Data Engineer, or Microsoft Azure Data Engineer certification programs to elevate your competence in data engineering.
Data Integration and ETL
ETL (Extract, Transform, Load) is a critical data engineering skill that involves extracting raw data from various sources, transforming it into a structured format, and consolidating it into a single repository, such as a data warehouse or business intelligence platform. This process is crucial for organizations seeking a comprehensive understanding of their data landscape, enabling them to make informed decisions.
With the emergence of Zero ETL, data engineers are exploring methods to eliminate the need for resource-intensive ETL processes. Zero-ETL streamlines the complexities of modern data pipelines, which are characterized by numerous points of integration and potential failure. This approach enables organizations to quickly access and analyze their data, underscoring the crucial role of data engineers in today's data-centric landscape.
Big Data Tools
Data engineers often work with massive amounts of structured and unstructured data, making it imperative for them to be competent in big data technologies. As a highly sought-after profession within the big data domain, data engineers must be adept at using tools such as Apache Spark, Apache Kafka, Hadoop, Hive, ELK Stack, Great Expectations, Segment, Snowflake, and Cassandra to excel in the field.
Data Engineers solve complex problems using automation to enhance accuracy, consistency, and efficiency in areas such as data collection, sanitization, cleansing, warehousing, integration, and reverse-ETL. This ensures the delivery of high-quality, complete, and easily accessible data to all stakeholders while meeting stringent requirements and maintaining data security and privacy.
Machine Learning Basics
Data engineers require ML skills, knowledge of algorithms, and familiarity with supervised and unsupervised learning techniques to effectively perform their job. They focus on creating end-to-end pipelines, ETL tasks, and working with regression, classification, and clustering methods.
By laying the groundwork for scientific research, data engineers enable stakeholders to gather and analyze raw data from multiple sources and formats, ensuring accurate models and supporting data exploration and analytical projects involving large datasets.
Got Questions about Data Engineering
We've got answers to your most frequently asked questions.
How to prepare for a data engineering interview?
To prepare for a data engineering interview, focus on developing your technical skills, such as SQL, Python, and big data technologies like Hadoop and Spark. Additionally, practice problem-solving and coding to enhance your logical thinking abilities. Familiarize yourself with data modeling, data warehousing, and ETL processes. It's also helpful to review common data engineering interview questions and practice explaining your thought process while solving problems.
What security skills does a data engineer need?
Data engineers need to be knowledgeable about data security best practices, such as encryption, access control, and secure data storage. They should also be familiar with relevant data privacy regulations, like GDPR and HIPAA. Data engineers must ensure that the data pipelines and systems they develop are secure and compliant with these regulations to protect sensitive information and prevent security breaches.
Is data engineering part of data science?
Data engineering and data science are closely related but distinct fields. Data engineering focuses on designing, creating, building, and maintaining data pipelines to collect and combine raw data from various sources, ensuring optimization. Data engineers work on developing data collection processes, integrating new technologies into existing systems, and streamlining systems for data collection and analysis.
Data science, on the other hand, uses a scientific approach to extract actionable business insights from data for decision-making. Data scientists often collaborate with data engineers to access and process the data needed for their analyses.
Can I get a job with a data engineering certification?
A data engineering certification can help you stand out in the job market and demonstrate your expertise in the field. High-quality data engineering certification courses can teach you advanced cloud computing and software engineering skills, which are valuable for a data engineering career. However, a certification alone may not guarantee a job. Employers also look for practical experience, problem-solving abilities, and a strong understanding of data engineering concepts. To increase your chances of getting a job, consider gaining hands-on experience through internships, personal projects, or contributing to open-source projects.
Data engineers analyze problems by breaking them down into components and then combining these pieces to create creative and effective solutions. To succeed in the field, they need strong technical skills, including proficiency in software engineering, big data technologies, and cloud computing. Data engineering is closely related to data science, with both fields working together to extract insights from data. A data engineering certification can help you stand out in the job market, but practical experience and a deep understanding of data engineering concepts are also crucial for securing a job.