Foundation in Programming

Proficiency in programming languages is crucial. Python and Java are commonly used in data engineering for scripting and building data pipelines. SQL is essential for database interaction. Familiarity with other languages like Scala or R can also be beneficial.

Big Data

​Knowledge of big data technologies is important as data engineers often deal with massive data sets. Technologies such as Apache Hadoop, Apache Spark, and HDFS are commonly used. Understanding distributed computing and storage principles is a part of this

Data Warehousing

Understanding different types of databases (such as relational databases like MySQL, PostgreSQL, and non-relational databases like MongoDB, Cassandra) is key. Knowledge of data warehousing solutions like Amazon Redshift, Google BigQuery, or Snowflake is also important for handling and analyzing large data sets.

Cloud Platforms

Proficiency in cloud services like AWS, Google Cloud Platform, or Azure is highly beneficial. This includes understanding how to leverage various cloud-based data solutions, storage, and computing resource

Data Pipeline

Experience with tools for Extract, Transform, Load (ETL) processes is critical. This includes familiarity with batch processing systems like Apache Hadoop or real-time processing systems like Apache Kafka, as well as ETL tools like Apache NiFi, Talend, or Informatica.

Data Security

Understanding data privacy, security best practices, and regulations (like GDPR or HIPAA) is crucial. Data engineers must ensure that data handling and storage comply with legal and organizational guidelines.