Entry-level data engineers typically start at around ₹7-8 lacs per annum, and they become data architects as they gain experience working with the company they're with. In India, a Data Architect can expect to earn an average of ₹21,68,063 per year. If you're looking for a job or looking to make more money, consider becoming a data architect.ĭata architects with over ten years of experience earn around $185k-200k per annum.ĭata Architect Salary in India Entry-Level Entry-level data engineers make around $75k annually. The average additional cash compensation for a Data Architect in the US is $49,491. The average salary for a Data Architect in the US is $144,244. For a good reason: they're responsible for designing and implementing data architecture solutions that allow companies to develop their products and services. Data Architect Salary According to Location and Experience Data Architect Salary in the US Entry-Levelĭata Engineers and Architects are in high demand in the US. You'll also need excellent communication skills to work closely with other organizational teams. To succeed at this job, you need strong technical skills in database design, programming languages like SQL or C++, computer networking (e.g., setting up firewalls), application development (e.g., building websites), etc. They also help implement it by setting up servers and databases, writing scripts that import and export data from other systems, creating user interfaces for accessing data in reports or dashboards, and setting up monitoring systems so admins can see how their systems are performing. They're also responsible for designing the architecture for a system. They also need to know how the various departments use those different types of information in an organization. They are responsible for designing, implementing, and maintaining the data management systems that power a company's business intelligence and analytics.ĭata architects have to keep track of all the different types of information that flow through a company, including structured data like customer records, unstructured data like emails and text messages, hierarchical data like directory structures, and tree-structured data like XML documents.

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