the most underrated data job in 2021

TL;DR

Data engineering is crucial and highly in demand in 2021.

Transcript

i've been noticing this trend myself but i didn't have the numbers to back it up until interview query released their annual report growth in data science interviews actually platformed in 2020 and grew by only 10 after previously growing by 80 year-over-year for the past few years yet data engineering specific interviews increased by 40 in the pas... Read More

Key Insights

• Data engineering interviews increased by 40% in 2020, highlighting the growing demand for the role compared to data science.
• Data engineers earn an average salary of $151k, surpassing the average salary of data scientists, which is $139k.
• Data engineers transform raw data into clean, accessible, and scalable formats, making it usable for data scientists and analysts.
• The role involves creating automated data pipelines to manage and process large volumes of data efficiently.
• Data engineers must continuously monitor and adapt their processes to ensure data integrity and address any issues arising from data source changes.
• They often collaborate with various business functions to ensure data-driven decision-making is effective and efficient.
• Key technical skills for data engineers include proficiency in SQL, Python, and an understanding of big data platforms like Spark or Hadoop.
• Unlike data science roles, data engineering positions often attract candidates with software engineering backgrounds.

Questions & Answers

Q: Why is data engineering considered the most underrated data job?

Data engineering is considered underrated because its demand has surged significantly, yet it remains less recognized compared to data science. Data engineers play a crucial role in transforming raw data into usable formats, which is essential for data-driven insights, making them indispensable in the data ecosystem.

Q: What are the primary responsibilities of a data engineer?

Data engineers are responsible for extracting, transforming, and loading (ETL) data from various sources into clean, accessible formats. They create automated data pipelines, ensure data accuracy and integrity, and collaborate with data scientists and analysts to facilitate effective data-driven decision-making within organizations.

Q: How does the salary of data engineers compare to data scientists?

Data engineers typically earn a higher average salary than data scientists, with data engineers earning around $151k compared to $139k for data scientists. This salary difference underscores the high demand and critical role data engineers play in managing and processing large volumes of data efficiently.

Q: What skills are essential for a data engineer?

Essential skills for a data engineer include proficiency in SQL and Python, understanding of algorithms, and knowledge of big data platforms like Spark or Hadoop. These skills enable data engineers to manage and process data effectively, ensuring it is clean and accessible for analysis and decision-making.

Q: How do data engineers ensure data quality and integrity?

Data engineers ensure data quality and integrity by continuously monitoring data pipelines, adapting to changes in data sources, and addressing any issues that arise. They implement automated processes to manage data efficiently and collaborate with various business functions to ensure the data meets organizational needs.

Q: Why is data engineering more aligned with software engineering than data science?

Data engineering is more aligned with software engineering because it involves building and maintaining data infrastructure, creating automated pipelines, and ensuring system efficiency. The role requires technical skills similar to software engineering, attracting candidates from such backgrounds, unlike data science, which often involves statistical analysis and modeling.

Q: What role do data engineers play in data-driven decision-making?

Data engineers play a crucial role in data-driven decision-making by ensuring that data is clean, structured, and accessible. They collaborate with data scientists, analysts, and business functions to provide the necessary data infrastructure, enabling organizations to derive insights and make informed decisions based on reliable data.

Q: How can one transition into a data engineering career?

Transitioning into a data engineering career involves acquiring key technical skills such as SQL, Python, and knowledge of big data platforms. Individuals with software engineering backgrounds may find the transition smoother due to the technical nature of the role. Gaining practical experience through projects or related work can also be beneficial.

Summary & Key Takeaways

• Data engineering has become a crucial and highly demanded role in 2021, with interviews for such positions increasing by 40%. This role is essential for transforming raw data into clean, accessible formats, enabling data scientists and analysts to derive insights effectively.

• The average salary for data engineers is higher than that of data scientists, highlighting the value and importance of this role. Data engineers are responsible for creating automated data pipelines and ensuring data integrity, which is crucial for data-driven decision-making.

• To become a data engineer, one needs proficiency in SQL, Python, and familiarity with big data platforms. The role is more aligned with software engineering, attracting candidates from such backgrounds, and involves collaboration with various business functions.