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Data engineer roles have gained significant popularity in recent years. Number of studies show that the number of data engineering job listings has increased by 50% over the year. Moreover, it is also becoming one of the most paid jobs according to Glassdoor. As we know, the more information we have, the more we can do with it. And data science provides us with methods to make use of this data. But, understanding and interpreting data is just the final stage of a long journey, as the information goes from its raw format to visual analytical boards. Processing data systematically requires a dedicated ecosystem where data is obtained, stored, processed, and queried. So, along with data scientists who create algorithms, there are data engineers and today’s article is about them. As it is a relatively new role, in this article we’ll explain what a data engineer is, key data engineer responsibilities and skill sets.
Who are data engineers?
While data science and data scientists in particular are concerned with exploring data, finding insights in it, and building machine learning algorithms, data engineering cares about making these algorithms work on a production infrastructure and creating data pipelines in general.
Data engineers are responsible for designing, maintaining, and optimizing data infrastructure for data collection, management, transformation, and access. The data engineer role evolved to handle the core data aspects of software engineering and data science; they use software engineering principles to develop algorithms that automate the data flow process. They also collaborate with data scientists to build machine learning and analytics infrastructure from testing to deployment.Data engineers help organizations structure and access their data with the speed and scalability they need and provide the infrastructure to enable teams to deliver great insights and analytics from that data.
Key Data Engineer responsibilities
- Cleaning and wrangling data from primary and secondary sources into formats that can be easily utilized by data scientists and other data consumers.
- Developing data tools and APIs for data analysis.
- Deploying and monitoring machine learning algorithms and statistical methods in production environments.
- Data engineers are in charge of building real-time data streaming and data processing pipelines.
- Data engineers are typically fluent in at least one programming language to create software solutions to data challenges. Python is regarded as the most popular and widely used programming language in the data engineering community.
- Data engineers assess a wide range of requirements and apply relevant database techniques to create a robust architecture.
- Data engineers implement methods to improve data reliability and quality.
- Data engineers build data pipelines that are used to transport data from a data source to a data warehouse.
- Find hidden patterns using data
- Use data to discover tasks that can be automated
Essential Data Engineer skills
Data engineers would closely work with data scientists mastering the following skills:
- Data Warehousing
- Data Architecture
- Object-oriented languages, such as Python , PySpark and Scala
- Machine Learning frameworks and libraries
- Expertise in data analysis
- BI tools knowledge
- Hadoop and Kafka
- Ingestion, processing, and surfacing of data
- Experience with Data Engineering tools such as Apache Beam, Spark, Kafka.
- Experience orchestrating ETL processes using systems such as Apache Airflow, and managing databases like SQL, Hive or MongoDB.
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