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What is an AI Data Engineer? 4 Important Skills, Responsibilities, & Tools

Monte Carlo

Proficiency in Programming Languages Knowledge of programming languages is a must for AI data engineers and traditional data engineers alike. In addition, AI data engineers should be familiar with programming languages such as Python , Java, Scala, and more for data pipeline, data lineage, and AI model development.

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Your Step-by-Step Guide to Become a Data Engineer in 2025

ProjectPro

A data engineer relies on Python and other programming languages for this task. You will use Python programming and Linux/UNIX shell scripts to extract, transform, and load (ETL) data. You will work with unstructured data and NoSQL relational databases. You will create PostgreSQL and Apache Cassandra databases using ETL.

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Data Engineering Roadmap, Learning Path,& Career Track 2025

ProjectPro

Good skills in computer programming languages like R, Python, Java, C++, etc. Computer Programming A decent understanding and experience of a computer programming language is necessary for data engineering. High efficiency in advanced probability and statistics.

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How to Transition from ETL Developer to Data Engineer?

ProjectPro

An ETL developer should be familiar with SQL/NoSQL databases and data mapping to understand data storage requirements and design warehouse layout. Although there are other query languages, SQL is the most often used for business purposes. SQL and Database Architecture Database architecture expertise is essential for an ETL developer.

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How to learn Python for Data Engineering?

ProjectPro

As demand for data engineers increases, the default programming language for completing various data engineering tasks is accredited to Python. One of the main reasons for this popular accreditation is that it is one of the most popular languages for data science. Python also tops TIOBE Index for May 2022.

Python 40
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How to Become an Artificial Intelligence Engineer in 2025

ProjectPro

A data engineer is expected to be adept at using ETL (Extract, Transform and Load) tools and be able to work with both SQL and NoSQL databases. They should also be fluent in programming languages like Python and should know basic shell scripting in Unix and Linux. Then, gain an understanding of how to work with shell scripting.

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How to Build an End to End Machine Learning Pipeline?

ProjectPro

For storing data, use NoSQL databases as they are an excellent choice for keeping massive amounts of rapidly evolving organized/unorganized data. The tool is not reliant on any particular library or a programming language and can be combined with any machine learning library.