Data Engineering

A data engineer is a professional who designs, develops, and maintains the infrastructure and systems required to store, process, and manage large volumes of data. They focus on the technical aspects of data, ensuring data pipelines, databases, and data architecture are robust, efficient, and scalable. 

General Business/Corporate Skills 

All Data Analysis Skills

Big Data Tools: Familiarize yourself with big data technologies such as Apache Hadoop, Spark, or Hive. Learn how to process and analyze large datasets using distributed computing frameworks. 

Data Warehousing: Understand the principles of data warehousing and ETL (Extract, Transform, Load) processes. Learn about tools like Apache Airflow or Apache Nifi for orchestrating and automating data pipelines. 

Cloud Platforms: Gain experience with cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. Understand how to leverage cloud-based services for data storage, processing, and analytics. 

Workflow and Job Scheduling: Learn about workflow and job scheduling tools like Apache Oozie, Apache Azkaban, or Luigi. These tools help orchestrate and manage data pipelines, ensuring smooth and efficient data processing. 

Distributed Systems: Develop an understanding of distributed systems and concepts like scalability, fault tolerance, and data partitioning. Learn about distributed file systems like Hadoop Distributed File System (HDFS) or object storage systems like Amazon S3.  

Version Control: Familiarize yourself with version control systems like Git. Learn how to effectively collaborate with other data engineers and manage code repositories for efficient development and deployment. 

Data Quality and Governance: Understand the importance of data quality and governance. Learn how to implement data quality checks, data validation processes, and data lineage tracking. Familiarize yourself with relevant regulations and compliance standards.