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Deena Chadwick
The CommonSensical BA

What's the Difference? Data Engineer vs. Data Architect

As more organizations become aware of the central role data plays in their business processes, there’s more demand for skilled workers to handle various data management tasks. But there’s also more confusion around the differences between positions like data architect and data engineer.

Data Engineer vs Data Architect

Data Engineer

Data Engineers are software engineers who build and integrate data from various parts of the data architecture and manage big data.

Data Architect

Data Architects design the “blueprint” for organizations Data Management. You may not be surprised to hear that this is a difficult job. Some companies need data architects who are ninjas in data modeling techniques; others may want experts in data warehousing, ETL tools, SQL databases or data administration. Data architects are likely to be senior-level employees with plenty of years in business intelligence under their belts.

Role

Owns technology platforms and datasets that enable systems and people to uncover new insights and fine-tune operations to meet business goals

Designing, creating, deploying, and managing an organization’s data architecture

Responsibilities

  • Apply broad knowledge of technology options, technology platforms, design techniques and approaches across the Data Engineering ecosystem to build systems that meet business needs
  • Build systems and datasets using software engineering best practices, data management fundamentals, data storage principles, recent advances in distributed systems, and operational excellence best practices
  • Analyze source systems, define underlying data sources and transformation requirements, design suitable data models and document the design/specifications
  • Demonstrate passion for quality and productivity by use of efficient development techniques, standards and guidelines
  • Effectively communicate with various teams and stakeholders, escalate technical and managerial issues at the right time and resolve conflicts
  • Collaborate with IT teams and management to devise a data strategy that addresses industry requirements
  • Build an inventory of data needed to implement the architecture
  • Research new opportunities for data acquisition
  • Identify and evaluate current data management technologies
  • Create a fluid, end-to-end vision for how data will flow
  • Develop data models for database structures
  • Design, document, construct and deploy database architectures and applications (e.g. large relational databases)
  • Integrate technical functionality (e.g., scalability, security, performance, data recovery, reliability, etc.)
  • Implement measures to ensure data accuracy and accessibility
  • Constantly monitor, refine and report on the performance of data management systems
  • Meld new systems with existing warehouse structures
  • Produce and enforce database development standards
  • Maintain a corporate repository of all data architecture artifacts and procedures

Other Similar Titles

Big Data Engineer

Software engineering backgrounds, design and build complex data pipelines, work closely with data scientist to put code into production, they typically use a lot of tools and are expert coders using Python, Java, Scala, and/or C++. Experience in Hadoop ecosystem, Spark, AWS, etc.

Business Intelligence Engineer

Data warehousing backgrounds, typically understanding/gathering business requirements to then design, and build reporting solutions. Support the data warehouses, ETL, dashboards, and reports. Tools/Technologies: SSIS, PowerBI, Tableau, RDBMS systems, MicroStrategy

Machine Learning Engineer

Hybrid role, bridges the gap between data engineering and data science, these folks come from data engineering backgrounds, have enough experience to be proficient in both data engineering and data science. They take what a data scientist finds and gets it ready for production. They create the last part of the DS pipeline. This could be rewriting code from R/Python to Java/Scala.

Computer Vision Engineer

Software engineering background, specializing in machine learning and/or deep learning techniques in relation to object detection, pattern recognition, face recognition, object tracking, etc. They are a mix between data and machine learning engineers and use tools like Python, C++, OpenCV, MATLAB, Java, and Spark. Oftentimes have a Master’s or PhD in Computer Science.