Data Architect

A Data Architect is an information technology professional responsible for designing, creating, managing, and optimizing an organization's data architecture to support data analysis, storage, and processing systems. They ensure data accessibility and integrity for efficient and accurate decision-making.
Salary Insights
High-ROI Certifications
Potential Lateral Jobs
Publications/ Groups

Data Architect

Payscale
Market
National (USA)
Base Salary
$130,110 / year
Satisfaction
Additional Benefits
Yes
Industry
All
Education
Bachelor's Degree

The average salary for Data Architect is $130,110 / year according to Payscale.com

There are no updated reports for Data Architect salaries. You can check potential lateral job opportunities in this information stack to find related salary information.

Data Architect role may have an alternate title depending on the company. To find more information, you can check Payscale.com.

Career Information

As a Data Architect, you will be responsible for designing and managing data architecture for organizations. You will need a strong understanding of database systems and data modeling techniques. Strong analytical and problem-solving skills are essential, as you will be responsible for designing efficient and scalable data solutions.

The average salary for Data Architect is $130,110 / year according to Payscale.com
AI Disclaimer
The following text about the Job role of Data Architect has been generated by an AI model developed by OpenAI. While efforts have been made to ensure the accuracy and coherence of the content, there is a possibility that the model may produce hallucinated or incorrect information. Therefore, we strongly recommend independently verifying any information provided in this text before making any decisions or taking any actions based on it.

A Data Architect is a professional who designs and manages an organization's data infrastructure. They play a crucial role in ensuring that data is stored, organized, and accessed efficiently and securely. 

One of the most important skills for a Data Architect is a deep understanding of database management systems (DBMS). They should be proficient in designing and implementing database solutions, such as relational databases or NoSQL databases, based on the organization's needs. They should also have knowledge of data modeling techniques and be able to create logical and physical data models.

Another essential skill for a Data Architect is expertise in data integration and ETL (Extract, Transform, Load) processes. They should be able to design and implement data integration solutions that consolidate data from various sources and transform it into a unified format. They should also have knowledge of ETL tools and techniques to automate the data integration process.

Data Architects are also responsible for ensuring the security and privacy of data. They should have a good understanding of data governance and compliance regulations, such as GDPR or HIPAA, and be able to implement security measures to protect sensitive data. They should also have knowledge of data encryption, access controls, and data masking techniques.

In addition to these technical skills, Data Architects should have strong analytical and problem-solving abilities. They should be able to analyze complex data requirements and design data solutions that meet the organization's needs. They should also be proactive in identifying data quality issues and implementing data cleansing and validation processes.

Data Architects often collaborate with other teams, such as data analysts or software developers, to ensure the effective use of data within the organization. They should have good communication and teamwork skills to effectively collaborate with these stakeholders and understand their data requirements.

Overall, a Data Architect plays a critical role in designing and managing an organization's data infrastructure. Their skills in database management, data integration, security, and problem-solving are essential for ensuring the efficient and secure use of data within the organization.

Potential Lateral Jobs
Explore the wide range of potential lateral job opportunities and career paths that are available in this role.

High-ROI Programs

Most roles require at least a bachelor's degree. To remain competitive, job seekers should consider specialization or skill-specific programs such as specialization, bootcamps or certifications.
Certification Programs
Consider pursuing specialized certifications or vendor-specific programs to enhance your qualifications and stand out in the job market.

Google Certified Professional Data Engineer

The Google Certified Professional Data Engineer is a high-ROI program designed for experienced professionals to become highly equipped with specialty skills and functional knowledge in cloud data engineering.

Advanced
View More

Microsoft Certified: Azure Data Engineer Associate

DP-203

The Microsoft Certified: Azure Data Engineer Associate program is designed for professionals who have expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems.

Intermediate
View More

Databricks Certified Data Engineer Associate

The Databricks Data Engineer Certification program is highly sought after, designed to evaluate an individual's proficiency in utilizing the Databricks Lakehouse Platform for performing fundamental data engineering tasks.

Intermediate
View More

AWS Certified Data Analytics — Specialty

DAS-C01

The AWS Data Analytics Certification program validates a deep understanding of AWS data analytics services and their integration with each other to derive insights from data, making it suitable for individuals pursuing a role focused on data analytics.

Advanced
View More

Microsoft Certified: Azure Enterprise Data Analyst Associate

DP-500

The Azure Data Analyst Certification is a high-ROI program designed for professionals who have expertise in designing, creating, and deploying enterprise-scale data analytics solutions.

Intermediate
View More

AWS Certified Machine Learning — Specialty Certification

MLS-C01

The AWS Certified Machine Learning - Specialty certification covers a wide range of topics, including data engineering, exploratory data analysis, modeling, and machine learning implementation and operations on the AWS Cloud. 

Advanced
View More

Google Certified Professional Machine Learning Engineer

The Google Machine Learning Certification is a high-ROI program designed for ML engineers who want to gain specialized machine learning skills using Google Cloud technologies.

Advanced
View More
Specialization Programs improving
If you want to improve your skills and knowledge in a particular field, you should think about enrolling in a Nanodegree or specialization program. This can greatly improve your chances of finding a job and make you more competitive in the job market.

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Nanodegree

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Intermediate
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Data: Law, Policy and Regulation

Executive Program

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Executive Program

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Specialization

Introduction to Data Science

Specialization

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IBM Data Analyst Professional Certificate

Professional

Genomic Data Science Specialization

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IBM Data Warehouse Engineer Professional Certificate

Professional

IBM Data Engineering Professional Certificate

Professional

Expand Your Knowledge of Artificial Intelligence

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Resource Stacks

We are soon crowdsourcing these resource stacks to collate the best resources, such as publications, community groups, job boards, etc., that are practically suitable for every contextual stack.
Publications
Discover the wide array of publications that professionals in this role actively engage with, expanding their knowledge and staying informed about the latest industry trends and developments.
Communities updating
Discover the thriving communities where professionals in this role come together to exchange knowledge, foster collaboration, and stay at the forefront of industry trends.
Research updating
We are currently in the process of updating contextual resources and we will be adding the new ones to the list shortly.
AI Disclosure: We are testing AI technologies to ensure the accuracy and coherence of recommendations. However, it is important to note that there is a possibility that the model may create hallucinated or incorrect inferences. Therefore, we highly recommend independently verifying any information provided in these stacks before making any decisions or taking any actions based on it.
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