Glossary

Data Engineer Architect

Who is a Data Engineer Architect?

A data engineer architect is a professional who has the responsibilities and skills of both a data engineer and a data architect. In the current market, these are often two separate professional roles.

A data architect helps conceptualize data frameworks that guide overall data modelling, data warehousing, database management, and any other ETL practices. Per popular definition, a data architect helps design how data is stored, consumed, and integrated for use by different IT systems and any applications that use the data in question.

By comparison, a data engineer helps build the pipelines that ingest, move, store, and prepare data based on the conceptualized data frameworks and architecture. Data engineers generally possess skills related to backend software engineering development and are well-versed with data pipeline management practices.

 

Why is the Data Engineer Architect Role Important?

With the advent of big data, the complexity of the data pipelines has exponentially increased. As the needs of enterprises continuously evolve, data pipelines and systems have to be agile enough to adapt to changing requirements. The data engineer architect’s role is at the heart of this complexity, not only to conceptualize and build these large-scale pipelines but also to maintain them over time for efficient consumption of compute resources and reliable usage by all applications.

 

How C3.ai Helps Data Engineer Architects

Data engineer architects can use the C3 AI® Type System to do most of the heavy lifting with respect to data unification and wrangling, through the canonical schema and application types. Through more than 200 pre-built data connectors and domain specific data models, the C3 AI Suite helps define, monitor, and manage data loading, lineage, relationships, and integration for complex source systems. Below is an example of the data loading process facilitated on the C3 AI Suite. The pre-built canonicals, data ingestion pipelines, and domain-specific data models help data engineer architects abstract away the dynamic nature and complexities of underlying source data from the application layer, to focus on delivering data in a consistent, reliable, and timely way.