1. What is the average salary of a Data Architect I?
The average annual salary of Data Architect I is $88,127.
In case you are finding an easy salary calculator,
the average hourly pay of Data Architect I is $42;
the average weekly pay of Data Architect I is $1,695;
the average monthly pay of Data Architect I is $7,344.
2. Where can a Data Architect I earn the most?
A Data Architect I's earning potential can vary widely depending on several factors, including location, industry, experience, education, and the specific employer.
According to the latest salary data by Salary.com, a Data Architect I earns the most in San Jose, CA, where the annual salary of a Data Architect I is $110,600.
3. What is the highest pay for Data Architect I?
The highest pay for Data Architect I is $108,721.
4. What is the lowest pay for Data Architect I?
The lowest pay for Data Architect I is $73,276.
5. What are the responsibilities of Data Architect I?
Data Architect I designs and builds databases for data storage or processing. Develops strategies for warehouse implementation, data acquisition and access, and data archiving and recovery. Being a Data Architect I builds data models and defines the structure, attributes and nomenclature of data elements. May evaluate new data sources for adherence to the organization's quality standards and ease of integration. Additionally, Data Architect I requires a bachelor's degree. Typically reports to a supervisor or manager. The Data Architect I work is closely managed. Works on projects/matters of limited complexity in a support role. To be a Data Architect I typically requires 0-2 years of related experience.
6. What are the skills of Data Architect I
Specify the abilities and skills that a person needs in order to carry out the specified job duties. Each competency has five to ten behavioral assertions that can be observed, each with a corresponding performance level (from one to five) that is required for a particular job.
1.)
Analysis: Analysis is the process of considering something carefully or using statistical methods in order to understand it or explain it.
2.)
Data Management: Data Management comprises all disciplines related to managing data as a valuable resource. The concept of data management arose in the 1980s as technology moved from sequential processing (first cards, then tape) to random access storage. Since it was now possible to store a discreet fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems." However, during this period, random access processing was not competitively fast, so those suggesting "process management" was more important than "data management" used batch processing time as their primary argument. As software applications evolved into real-time, interactive usage, it became obvious that both management processes were important. If the data was not well defined, the data would be mis-used in applications. If the process wasn't well defined, it was impossible to meet user needs.
3.)
Data Warehousing: Data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence.