How To Tell The Difference Between A Data Architect & Data Engineer

Data Architect (1)

The difference between a data architect and a data engineer is simple. Both fall under the Data Science career discipline for making “big data” usable.

Data engineers are responsible for the day-to-day tasks of bringing data into a system, wrangling it, cleaning it, and making it ready for analysis. They use a variety of tools to accomplish this, and they generally specialize in one area (relational databases, data warehouses, etc.).

A data architect is more strategic in their thinking. They are involved in the enterprise architecture and the process of extracting, transforming, and loading data, but they think about how the data should be structured and organized as the data is added to the system. When data architects and engineers work together, they can produce high-quality usable data for executive decisions.

Why are these data roles important today?

Bottom line, data jobs are important because they allow humans to make better decisions based on actionable insights created. According to International Data Corporation (IDC), by 2025, the volume of data created will “swell up to 175 zettabytes (ZB) which will be a ten-fold increase” of the total digital data created by consumers and businesses alike. This research also suggests that businesses will make up 60% of the data where consumers were the primary creators previously.

The ability to handle these large volumes of data can be an overwhelming task for any organization. That’s where data engineers and data architects collaborate and join forces to form an efficient and robust data management framework.

big data market

Big Data Revenue Projections by Statista

What does a Data Engineer do?

A data engineer is the broadest of the computer science titles engineers. A data engineer can work on any part of a data project, from designing a data pipeline to implementing a data warehouse to collecting and cleansing data, to programming an algorithm to statistical analysis. They are the jack-of-all-trades when it comes to data. They are problem solvers who are the ones who make sense of the data.

As data volumes continue to grow, the specific roles of a data engineer are becoming more defined. Data engineers are responsible for designing scalable data processing pipelines that can store and update large volumes of data, as well as generate insight based on the information gathered. The data engineer should have a strong understanding of the different approaches to big data processing, as well as the characteristics of the data itself.

What does a Data Architect do?

Data architects are responsible for taking the data that is available to a company and making sure that it can be accessed and understood by those that need to use it, and that the data is available to those who need it in the most appropriate form.

Many newcomers to data architecture believe it’s a discipline that only involves data modeling, but that’s only part of what data architects do. As the requirements of data architects evolve, their job description changes too. Data architects look at the entire enterprise architecture and combine deep knowledge of the business goals and data requirements of an organization with a technical understanding of how technology can be used to achieve those goals, to design and implement a strategy that incorporates all of the data assets of an enterprise, regardless of where they’re stored or how they’re used.

Data architects work closely with business analysts, database administrators, engineers, and application developers to build a cohesive data-driven application that provides the business with a strategic competitive advantage. Data architects also advocate for the ongoing growth and improvement of the systems.

What do Data Engineers and Data Architects Have in Common?

Generally, data engineers and data architects collaborate and work side-by-side.

Here are a few overlapping skills:

  • Extract, Transform, Load (ETL)
  • Data warehousing
  • Programming knowledge (Python/R/Java)
  • Data modeling
  • Deep knowledge of SQL/databases
  • Programming knowledge (Python/R/Java)
  • Data architecture and pipeline knowledge
  • Machine learning conceptual knowledge
  • Data visualization, scripting, reporting

Compare Data Engineer and Data Architect Role:

Build and maintain data frameworks and architecture Conceptualize and visualize the data framework an enterprise level
Gather and process raw data Use ETL tools (Extraction Transformation and Load), spreadsheets and business intelligence tool knowledge
Strong software engineering background Creates the roadmap for data management systems to integrate, centralize, protect and maintain data sources
Maintain data pipelines and databases Data modeling and data administration knowledge/deep database expertise
Algorithms (machine learning framework knowledge and libraries) Machine learning knowledge
Data ingestion and storage Data warehousing solutions and strong deep knowledge of database architecture
Design and create data applications Systems development
R, Python, SAS, Hive, MatLab, SQL, NoSQL, Pig, Hadoop, Java, C/C++ SPSS, Ruby, Perl, MapReduce, MongoDB, Cassandra, Hbase, Cloudera, Scala, GoLang, Kafka, Data Streaming
Hive, SQL, Pig, Spark, XML, ETL (Informatica, Talend etc.), NoSQL, Modeling (ie. Erwin etc.) and Visualization tools (ie.Visio etc.), Unix, Linux
EDUCATION: Bachelors in a technical field; Certifications that are helpful include ones from AWS, Azure, Cloudera, DASCA, Microsoft, IBM, SAS, Oracle. EDUCATION: Bachelors or Masters in a technical field and at least 4 years in the data field. Certifications that are helpful include ones from AWS, Cloudera, and Google.
Average salary $92,305/yr  Average Base Salary  – $65k-$132K Total pay – $65K-$140K depending on bonus and profit sharing
Average salary $119,400/yr  Average Base Salary – $77k-$156K Total pay – $78K-$176K depending on bonus and profit sharing
*Salaries based on

What non-technical skills make a good Data Engineer?

Data engineers are the ones who keep data flowing through an organization. They provide the infrastructure and tools that keep the data flowing. They are often considered the glue that holds the data team together. They are not usually involved in the day-to-day analysis of data, but rather work behind the scenes to support the team. Beyond the technical skills, there are some soft skills needed as well.

  • Ability to fully analyze issues and then create solutions that are unique and effective.
  • Critical thinking skills to make better decisions and creates a thorough researcher to provide stakeholders more reliable recommendations
  • Leadership, communication, and people skills to work with other engineers and architects

What non-technical skills make a good Data Architect?

Data architects design the data strategies and data models for the enterprise. They are generally business leaders that provide direction, insight, advice, and support for an enterprise’s information, so the non-technical skills of an architect are very important.

  • Ability to have strong problem-solving skills from an analytical standpoint as an architect will be working with a variety of technologies
  • Negotiation and people skills to work with different leaders and the ability to listen to what is needed by management and other related staff.
  • Writing and presentation skills
  • Designing the most effective approach to make the most out of the resources
  • Domain knowledge to properly understand what approach is needed based on industry operations.
  • Strong leadership to successfully manage other architects, engineers, modelers, and database administrators.

Professional Certifications for Data Engineers and Data Architects

There are various professional industry certificates available to Data Architects and Data Engineers. The Institute for Certified Computing Professionals (ICCP) offers one of the most respected courses known as the Certified Data Management Professional (CDMP) certification. You have the option to earn this certification either at the “practitioner” or “mastery” level.

Similar certifications offered by Google and IBM are Certified Professionals in Data Engineering and IBM Certified Data Engineer in big data respectively.

The CCP Data Engineer from Cloudera and the Microsoft Certified Solutions Expert certification in data management and analytics are also amongst well-known certifications.

Other popular certifications among data industry professionals:

These certifications consolidate technical skills and experience in many areas of data. They are designed to certify a candidate’s capabilities to a particular level and in many instances have an impact on compensation.

Which Career Path Should You Follow?

The data science area is a growing field due to the amount of data available and the increased use of machine learning and artificial intelligence learning from the data.

In the years to come, data analysts, data scientists, data architects, and data engineering jobs will hold a significant percentage of the job market due to an unprecedented rise in the number of data sources and interconnected data pipelines available.

Focus on what you truly like to do! Each data role will either depend more on reporting, programming, statistics, or a combination. To will help guide you, check out the article we wrote about jobs you can get with a Data Science degree!