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Data Solutions Architect

No of Positions  No of Positions:   1

location Location: Pune

date Tentative Start Date:   September 19, 2022

Work From Work From : Any Location

rate Rate : $ 8  -  24 (Hourly)

experience Experience : 5 to 10 Year

Job Applicants : 4
Job Views : 162
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Job Category : Information Technology & Services
Duration : Long-Term
Key Skills Required Skills
Cloud Architect Big Data Hadoop ETL Architecture

● Advance Data Architecture practices and standards across the enterprise

● Establish enterprise data strategy, target and reference architectures and align with Data Residency and Processing requirements across global regions

● Create, publish and maintain data related technology standards

● Ensure ongoing integrity and accuracy of enterprise data model

● Lead data based solution architecture efforts

● Work closely with data teams across the enterprise to collect and translate information requirements into data-centric solutions and strategies

● Provide architecture and consulting services to solution teams

● Collaborate with solution teams on design and integration efforts

● Participate in advancing architecture discipline across the enterprise

● Evaluate Cloud platform data services

● Conduct product evaluation and provide recommendations

● Perform architecture analysis and provide improvement recommendations

● Define Target Architecture and Operational Models as needed

● Analyze legacy systems and provide Target and Transition Architecture in compliance with Enterprise Architecture TAM and reference models ● Work with Compliance and Security teams to integrate their requirements into Architecture practices

● Establish functional and non-functional requirements and test cases and plans

● Participate and advance automation efforts across the Enterprise

● Create Reference Architecture and Building Blocks

● Responsible for creating, maintaining, and adoption of technology standards

● Identify opportunities for improvement of data quality, accessibility and security through streamlining data flows and models, improving consistency of data and unification of the data architecture

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