Is learning data engineering worth it?

With the rise of big data, data engineering has become one of the most in-demand tech skills. Data engineers build and maintain the infrastructure that collects, stores, and manages data for analysis. As more companies adopt data-driven strategies, they need data engineers who can build scalable and reliable data pipelines. So is learning data engineering a worthwhile investment of your time and energy? Let’s weigh the pros and cons.

The growing demand for data engineers

The demand for data engineers has exploded in recent years. As per LinkedIn’s 2020 Emerging Jobs report, data engineer jobs have grown over 34% annually for the last three years. The Bureau of Labor Statistics also projects above-average job growth of 11% for database administrators and architects between 2019 to 2029. This is over 3 times faster than the average for all occupations. Some key drivers of this demand include:

• The move towards cloud computing – As more companies adopt cloud-based infrastructure like AWS, GCP and Azure, they need engineers to build and maintain cloud data pipelines and systems.
• The data analytics boom – With the rise of data-driven decision making, companies are investing heavily in data analytics. All this analytics require data infrastructure built by data engineers.
• Artificial intelligence – AI applications like machine learning rely on massive training datasets. Data engineers construct the platforms to collect, clean and label the volumes of data needed.

In short, data engineering sits at the heart of most modern technologies dealing with data. This critical role explains the surging demand. Data engineers often command high salaries as well. According to Glassdoor, the average data engineer in the US earns $117,785 annually as of 2020.

High job satisfaction

Data engineering offers intellectual stimulation and high job satisfaction scores. In Stack Overflow’s 2020 developer survey, 69% of data engineers described themselves as either very satisfied or satisfied with their jobs. This placed data engineering among the most satisfying software roles. Data engineering provides some key perks like:

• Complex problem solving – Data engineers get the intellectual challenge of solving complex data problems and building efficient pipelines.
• Continuous learning – You continually learn new tools and technologies in this fast-moving field.
• High impact – Your work enables key business insights and decisions through analytics.
• Good work-life balance – Data engineering teams often have more reasonable on-call schedules than some software roles.

Overall, data engineers get to do intellectually stimulating work that delivers a lot of impact. The high satisfaction scores reflect this engaging mix of problem solving and learning.

High pay

As mentioned earlier, data engineering offers pay packages substantially higher than average IT salaries. Here’s a look at typical data engineer salaries in the US as per Payscale:

These attractive salary ranges are well above average figures for software developers and engineers. With some years of experience, you can expect total compensation packages at FAANG companies exceeding $250,000. Other in-demand skills like cloud, security or machine learning expertise can help you command even higher salaries.

Wide range of career paths

Data engineering skills open up many potential career directions. After gaining some initial experience, you can choose to specialize in areas like:

• Cloud data engineering – Focus on cloud platforms like AWS, GCP and Azure.
• Data warehousing – Build enterprise data warehouses and lakehouses.
• Data integration – Focus on integrating diverse datasets.
• Machine learning engineering – Provide data platforms for ML models.
• Analytics engineering – Build pipelines for business analytics use cases.

Many data engineers also progress into data architecture or management roles. Some move into specialized domains like healthcare, finance or retail. Others may even leverage their knowledge to transition into data science or machine learning engineering roles. This versatility provides diverse career options as you advance.

High barriers to entry

While data engineering offers many perks, it does present high barriers to entry. Typically, you need:

• A bachelor’s degree in computer science or engineering.
• Proficiency in languages like Python, Scala, Java, SQL, etc.
• Knowledge of data processing frameworks like Spark, Kafka, Flink, etc.
• Familiarity with data pipelines, ETL, data modeling and warehousing.
• Experience with infrastructure like Linux, containers, cloud platforms.

Gaining expertise across this diverse technology stack takes consistent effort. Data engineering teams also favor engineers who can handle complex distributed systems. Mastering the field requires patience and persistence for continuous learning.

Tough competition

The high demand for data engineers has led to a very competitive job market. Data engineering roles at top companies often attract hundreds of applicants. Standing out requires strong technical knowledge and hands-on skills. Some strategies that can improve your chances include:

• Contributing to open source data engineering projects.
• Completing internships and co-ops while in college.
• Building a portfolio of data pipelines and projects.
• Getting certified in technologies like AWS, GCP, Spark, Kafka etc.
• Having pet projects that demonstrate your skills.

With the popularity of data, engineering roles receive intense interest. You need to build up skills and experience to thrive in this competitive landscape.

Constant change

Data engineering requires constant learning because of swift innovation in the field. To stay relevant, data engineers have to keep updating their skills as new tools and techniques emerge. For example, the rise of cloud has required engineers to adapt. Here are some of the evolving technologies to keep pace with:

• Cloud platforms like AWS, GCP and Azure.
• New data processing options like Spark, Flink, Kafka etc.
• Changing data formats like Parquet, ORC, Avro, JSON etc.
• Containerization with Docker and Kubernetes.
• Data modeling approaches like data vaults, data lakes etc.

The rapid rate of change makes continuous learning essential. Data engineers have to constantly look out for emerging best practices and update their skills. Keeping up with the latest technologies is key for career growth.

Stressful on-call schedules

Data engineering teams need to ensure that data pipelines run smoothly at all times. This results in on-call rotations where engineers get paged if production issues emerge. Some common examples include:

• ETL pipelines failing due to data quality issues.
• Data warehouse failures due to capacity limits.
• Peak data flows overwhelming Kafka queues.
• Database deadlocks affecting transaction integrity.

Handling such incidents at odd hours can disrupt work-life balance. While on-call schedules are improving, the mission-critical nature of data infrastructure makes complete avoidance difficult.

Should you become a data engineer?

Based on the pros and cons, should you consider becoming a data engineer? Here are some key factors to weigh:

• If you enjoy complex technical problem solving, data engineering provides engaging work.
• The high pay, demand and job satisfaction make it rewarding for the right individuals.
• It’s a solid option if you prefer depth of expertise over breadth.
• But you need patience and strong foundations in computer science, coding and math.
• Expect a steep initial learning curve and the need for continual education.
• The work can be stressful due to critical data pipelines and on-call schedules.

Overall, data engineering is an appealing career with good rewards. But the barriers to entry are high. It requires diligent effort to gain proficiency and keep skills current. If you enjoy coding and data infrastructure, the investments needed to become a data engineer will pay rich dividends.

Conclusion

Data engineering offers attractive benefits like high pay, strong demand and engaging work. But it requires patience to amass the diverse skills needed. Continuous learning is also a must given the speed of change. For individuals with solid software and data foundations, as well as an interest in infrastructure, data engineering can be a fulfilling career choice. Weigh the pros and cons outlined here to decide if becoming a data engineer is worth the investments required.