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Big Data Engineering

Successful digitization Requires high quality data!

Big Data Engineering are the ones who design & develop data, they are the brains behind data collection from various sources. Their responsibilities vary from company to company, and they are in high demand.

Whether you want to provide your management with current company figures or your customers with AI-based products, You can only achieve reliable results with high quality data. It is also important to enable efficient and easy data access. A competent big data engineer achieves all of this. It is thereby an essential prerequisite for the successful digitization of your company.

What does a big data engineer actually do?

Big Data engineering is crucial to any tech-driven organization function is to develop, test, and maintain big data architectures, data pipelines, warehouses, and other processing systems. A data engineer’s ultimate goal is to retrieve, store, and distribute data throughout an organization.

Big data engineering responsibilities

  • Design, create, and manage scalable ETL (extract, transform, load) systems and pipelines for various data sources
  • Manage, improve, and maintain existing data warehouse and data lake solutions
  • Work closely with business intelligence teams and software developers to define strategic objectives as data models, Explore the next generation of data-related tech to expand the organization’s capacity, and maintain a competitive edge

skills for Big Data Engineers

  • Critical thinking, excellent communication, team working, and problem-solving
  • Several years of experience in software development or data management
  • Experience using relational database management systems, e.g. PostgreSQL, MySQL
  • Understanding of batch and real-time data integration, data replication, data streaming, virtualization, and so on

What tools does a big data engineer use?

  • Python (and other programming languages)
  • ETL tools
  • SQL and NoSQL
  • PostgreSQL (or another database management system)
  • Apache Spark (and to a lesser extent, Hadoop)
  • Amazon S3
big data

What tools does a big data engineer use?

  • Python (and other programming languages)
  • ETL tools
  • SQL and NoSQL
  • PostgreSQL (or another database management system)
  • Apache Spark (and to a lesser extent, Hadoop)
  • Amazon S3