What Does a Data Engineer Do?
Data engineering is an exciting and fast-evolving field. Organizations are collecting, storing, and analyzing more data than ever before, and they need professionals who are highly skilled at designing, building, and optimizing systems that help them extract the most value from their data, which now comes in many types, including structured and unstructured as well as real-time and batch. Let’s look at the core skills required to become a data engineer, options for specialization, and top training opportunities for those interested in data engineering.
What Does a Data Engineer Do?
Data engineering is a diverse profession with a range of specializations. For this reason, different data engineers may have different job responsibilities. Generally speaking, data engineers are responsible for developing, testing, and maintaining data pipelines, for converting raw data into usable formats, and for organizing the data so that it can be used efficiently. They’re also experts in their organization’s business goals to ensure that the company’s data strategies are aligned to support its goals. Data engineers are also typically involved in communication and collaboration with a wide variety of stakeholders, from data analysts to the C-suite.
Must-Have Skills for a Data Engineer
Data engineers are highly skilled professionals with a combination of technical expertise, business acumen, and problem-solving savvy. Because the specific skills required depend on the job and specialization, there’s no authoritative list of skills needed to become a data engineer. However, most data engineers are proficient in the following areas:
Programming languages: Most data engineers will be expected to know Python and SQL, but Java, Node.js, and others may be required depending on the role.
Database management: Data engineers are the ones who must ensure the appropriate infrastructure is available to the users and applications consuming the outputs of their data pipelines. They must also diagnose and resolve any faults or errors.
Cloud data storage: Data engineers must be familiar with the various cloud platforms their organization uses to store, process, and manage data.
Streaming pipelines: More organizations are beginning to capture the value of data in real time using streaming data pipelines. To support real-time analytics, data engineers must know how to ingest, process, and serve the data needed to build them.
Data analysis: Analysis is typically the domain of data scientists and analysts. But because data engineers collaborate closely with both of these teams, they should understand the basics of data analysis and statistical modeling as well as the various analytics tools their organization uses.
Business acumen and domain knowledge: Data engineers must have a firm grasp of their organization’s goals and business strategies to communicate effectively with their leadership teams. They also need domain knowledge, since crucial nuances in the meaning of the data may be otherwise missed. Data engineers often collaborate with domain experts such as marketing teams, sales managers, and finance teams to better understand their areas of expertise.
Governance and security: Data engineers must understand governance and security best practices as well as their organization’s policies, procedures, and protocols to maintain the required regulatory and privacy compliance.
Creativity and critical thinking: Data engineers do a lot of problem-solving in their day-to-day work. For this reason, they need to creatively come up with possible solutions to challenging problems and exercise critical thinking skills.
Basic knowledge of machine learning (ML): Again, due to collaboration with data scientists, data engineers need a basic understanding of ML models and statistical analysis.
How to Become a Data Engineer and Decide on a Specialization
Data engineers play a vital role in supporting an organization’s data strategy, but each business will have a unique set of data engineer job requirements tailored to meet the needs of its data strategy. Smaller companies may even task their data engineer with some of the job responsibilities typically reserved for data scientists, while larger ones that employ multiple data engineers will require specialization. Here are three primary data engineering roles and a brief description of each:
Data engineering generalist
Generalists typically work for smaller organizations, filling numerous roles that may each be filled by a data engineer with specialized experience in a larger business. Data engineers in these work environments may complete some tasks traditionally assigned to data scientists such as analyzing the data for actionable insights and interpreting the findings for key stakeholders. This position is often ideal for someone with entry-level skills or for those seeking to cross over from a related field like data science or data architecture.
Organizations large enough to employ multiple data engineers may choose to seek one or more who specialize in building and maintaining data pipelines. Data pipelines are used to pull data from multiple sources, often aggregating it together into a cloud platform or cloud data warehouse. Along the way, the data is transformed and optimized, making it available for analytics and operational uses. Midsize companies often employ data engineers with a pipeline-focused skill set.
Some data engineers are responsible for the design, construction, and installation of data systems required to support machine learning applications. ML-focused data engineering positions are typically found in large organizations with data needs necessary to support this level of specialization.
Data Engineer Training
Whether you’re interested in transferring from a related field or just starting your career, there are numerous options for getting the training required to begin a career in data engineering.
Experience in a related field
Professionals with career experience in related fields such as computer programming, computer science, database administrator, database engineering, business intelligence, or data science will have an easier time transitioning into the role of a data engineer. Many of these positions have an overlap in skills, making it easier to transfer them to a new application.
College training programs
As the demand for data engineers continues to grow, numerous colleges are offering credential programs for data engineers. Training options include both in-person and 100% virtual options.
Bootcamps are an excellent way to learn a lot of career-ready skills in a relatively short amount of time. These intensive training programs cater to a wide audience. Whether you have no relevant data engineering skills or you’re a seasoned professional looking to add a new area of specialization, you can find a bootcamp designed to meet your needs.
Learning on the job is one of the best ways to gain job-ready skills. Enrolling in a formal internship program gives you a front-row seat into the daily work involved in data engineering, providing valuable firsthand experience working through the challenges facing today’s data engineers.
Move Your Career Forward with Snowflake Certifications and Live Trainings
Whether you’re crossing over from another data-centered career or looking to maintain your edge, Snowflake certifications and live training provide the knowledge you need to move your career in data engineering forward. Snowflake’s live training is an excellent way to enhance your existing skill set with cutting-edge instruction for getting the most out of Snowflake.
Snowflake certifications are ideal for data engineers interested in setting themselves apart as Snowflake experts. With two credential tracks, the SnowPro Core Certification and SnowPro Advanced Certification, Snowflake users with varying experience levels have the opportunity to demonstrate their skills with working in Snowflake. The SnowPro Core Certification demonstrates proficiency in applying core expertise implementing and migrating to Snowflake. The SnowPro Advanced Certification is geared toward those with extensive industry experience working in Snowflake. This advanced-level certification series consists of five role-based credentials including standalone certifications for numerous roles including architect, administrator, data engineer, data scientist, and data analyst (available in late 2022). As Snowflake’s importance in supporting advanced data engineering continues to grow, demonstrating proficiency using this technology has become a strategic advantage for data engineers looking to advance their careers.