Data engineering

Empowering Innovation with Data Engineering

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts.

In addition to making the lives of data scientists easier, working as a data engineer can give you the opportunity to make a tangible difference in a world where we’ll be producing 463 exabytes per day by 2025 . That’s one and 18 zeros of bytes worth of data. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data.

What does a data engineer do?

Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

Common tasks a data engineer

Some of the common tasks a data engineer might perform when working with data include:

cquire datasets that align with business needs
Develop algorithms to transform data into useful, actionable information
Build, test, and maintain database pipeline architectures
Collaborate with management to understand company objectives
Create new data validation methods and data analysis tools
Ensure compliance with data governance and security policies

Evolution of the Data Engineer

It has never been a better time to be a data engineer. Over the last decade, we have seen a massive awakening of enterprises now recognizing their data as the company’s heartbeat, making data engineering the job function that ensures accurate, current, and quality data flow to the solutions that depend on it.

Responsibilities of a Data Engineering Team

The Data Engineering team should provide a shared capability within the enterprise that cuts across to support both the Reporting/Analytics and Data Science capabilities to provide access to clean, transformed, formatted, scalable, and secure data ready for analysis. The Data Engineering teams’ core responsibilities should include:

Build, manage, and optimize the core data platform infrastructure
Build and maintain custom and off-the-shelf data integrations and ingestion pipelines from a variety of structured and unstructured sources
Manage overall data pipeline orchestration
Manage transformation of data either before or after load of raw data through both technical processes and business logic
Support analytics teams with design and performance optimizations of data warehouses