Data engineering is a fast-growing field that plays a critical role in managing and utilizing data in the era of big data. Data engineers are responsible for building and maintaining data pipelines that collect, transform, store and move data. With the exponential growth of data across industries, companies are investing heavily in data infrastructure and analytics, driving up demand and salaries for skilled data engineers.
But do data engineers really make good money? Let’s take a detailed look at data engineer salaries, skills required, career prospects and more to better understand how lucrative this career is.
Data Engineer Salaries
According to Glassdoor, the average base salary for a data engineer in the United States is $117,785 per year, with salaries ranging from $92,500 at the 25th percentile to $150,000 at the 75th percentile.
Location greatly impacts data engineer salaries:
Data Engineer Salaries by Location
Location | Average Base Salary |
---|---|
San Francisco, CA | $150,529 |
Seattle, WA | $138,818 |
New York, NY | $135,987 |
Boston, MA | $123,229 |
Chicago, IL | $120,046 |
Atlanta, GA | $117,427 |
Washington, DC | $116,893 |
As you can see, data engineer salaries in major tech hubs like San Francisco and Seattle exceed $130,000 on average. But even in other large metropolitan areas, base compensation remains above $115,000.
In addition to geographic differences, data engineer salaries vary based on experience and company:
Data Engineer Salaries by Experience
Experience | Average Base Salary |
---|---|
Entry Level (0-2 years) | $96,500 |
Mid-Level (3-5 years) | $118,250 |
Experienced (6-9 years) | $133,750 |
Expert (10+ years) | $148,000 |
As expected, salaries steadily climb with more experience, increasing by over $50,000 from entry level to expert. However, even entry level data engineers can expect to earn near or above six figures.
Data Engineer Salaries by Company Size
Company Size | Average Base Salary |
---|---|
Small (<250 employees) | $108,529 |
Medium (250-1000 employees) | $121,818 |
Large (>1000 employees) | $130,987 |
Bigger companies tend to pay data engineers higher salaries, likely due to larger data infrastructure needs and budgets. But even at small companies, average compensation remains over $100,000.
In summary, data engineers earn well-paying salaries that increase with experience and company size. Even at entry level or with smaller employers, data engineers can expect six figure compensation on average nationwide.
Data Engineer Responsibilities
To understand why data engineers command such high salaries, let’s look at what they actually do:
– Design and build scalable data architecture – Data engineers develop automated, optimized systems for collecting, storing and accessing large datasets. This requires expertise in data processing tools like SQL, Spark, Hadoop, etc.
– Develop data pipelines and workflows – Using ETL (extract, transform, load) processes, data engineers construct reliable pipelines that take raw data from source systems, apply any cleaning or transformations, and load it into target databases or data warehouses.
– Create database schemas and data models – Data engineers define database schemas tailored to the specific analysis needs of their company or clients. This requires understanding how data will be queried and consumed.
– Ensure high data quality and integrity – Data engineers test data pipelines, troubleshoot issues, and perform checks to guarantee completeness, validity and accuracy of data flowing through the pipelines.
– Monitor and optimize data pipeline performance – Data engineers keep close watch on data pipeline metrics like throughput, latency and uptime. They identify and resolve bottlenecks to improve efficiency.
– Implement data security and access controls – Since data pipelines contain sensitive information, data engineers build proper access controls, encryption, masking and monitoring to protect against data breaches or misuse.
In summary, data engineers build complex data infrastructures that power a company’s analytics capabilities at massive scale. Their work requires both strong technical skills and business understanding. When done well, data engineers save companies time and open up new opportunities through reliable access to high quality data.
Data Engineer Skills
Given their technical responsibilities, data engineers need a diverse set of in-demand skills:
– Programming – Python and Scala are common languages for data engineering. SQL is a must-have skill for working with databases.
– Cloud platforms – Experience with AWS, GCP, Azure or similar for building cloud-based data solutions at scale.
– Big data – Expertise in distributed systems like Hadoop, Spark, Kafka for storing and processing large datasets.
– Databases – Knowledge of both SQL and NoSQL databases like MySQL, Oracle, MongoDB, Cassandra, etc.
– ETL tools – Experience with extract, transform and load tools like Informatica, Talend, Pentaho for building data pipelines.
– Workflow orchestration – Familiarity with workflow managers like Apache Airflow, Azkaban, Oozie for scheduling and monitoring pipelines.
– Streaming data – Understanding of handling real-time data streams from sources like IoT devices, social media feeds, etc.
– Data modeling – Ability to develop entity relationship diagrams, dimensional models, data pipeline schemas.
– Analytics and visualization – Data exploration skills to validate outputs and communicate insights.
This combination of software engineering, data and business skills enables data engineers to effectively design, implement and evolve complex data infrastructures. Mastering these areas is essential for succeeding and advancing as a data engineer.
Data Engineer Demand and Job Outlook
Strong salaries directly correlate with the tremendous demand for data engineers. By 2024, the market intelligence firm IDC predicts worldwide revenues for big data and business analytics will reach $274.3 billion.
Every company now understands the competitive necessity of data-driven decision making. But realizing this data advantage requires specialized infrastructure and personnel.
LinkedIn’s 2020 Emerging Jobs Report identified Data Engineer as the #1 emerging job for 2020. LinkedIn notes title mentions of data engineer grew almost 50% YoY, signaling surging demand.
The U.S. Bureau of Labor Statistics groups data engineers under the larger category of database administrators and predicts 13% growth in this field from 2019 to 2029 – much faster than average occupational growth.
With the huge influx of data from 5G networks, IoT sensors, social platforms and more, demand for data engineering skills will only intensify. Companies urgently need data engineers to make their data useful and differentiated.
Combined with highly competitive salaries, excellent job outlook and dynamic work, data engineering emerges as an appealing long-term career choice.
Advancement Prospects for Data Engineers
A major advantage of data engineering is the multitude of advancement options available as your experience grows. Some potential career progressions include:
– Data Architect – Architect enterprise-wide data systems and set standards for data practices company-wide. Requires leadership skills and deep technical expertise.
– Analytics Engineer – Focus on developing systems for advanced analytics and machine learning modeling, which are key emerging areas.
– DataOps Engineer – Specialize in greater automation and collaboration in data pipelines through integrating DataOps tools and culture.
– Principal/Lead Data Engineer – Manage a team of data engineers and architect complex data platforms.
– Data Platform Owner – Take ownership of a company’s entire data platform and make strategic data-related decisions.
– Data Science/Machine Learning – Leverage your data skills to move into more advanced analytics and modeling roles.
– Engineering Management – Lead teams of data professionals as a CTO, Director of Engineering or VP of Data.
– Start a Company – Found a data-driven technology or consulting startup to advance the state of the art.
Data engineers also have opportunities to master highly valued adjacent skills like cloud platforms, cybersecurity, and software architecture. Overall, data engineers have an abundance of options to take on greater responsibility and impact as their careers progress.
Conclusion
In today’s data-first world, skilled data engineers earn well-deserved high compensation. But beyond economic rewards, data engineers enjoy working on cutting-edge technologies, collaborating cross-functionally, and enabling data-driven decision making with substantial business impact.
With strong technical acumen coupled with communication skills, data engineers can build, maintain and lead advanced data platforms at exciting high-growth companies. The field offers ample career advancement potential through specialization or management.
In summary, data engineering delivers on all aspects of a rewarding career – compensation, challenge, impact and options. Aspiring and experienced data professionals will find data engineering offers an excellent path to maximize their career potential.