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Open nowFirst seen 3 days ago

Architect and Project Manager

PradeepIT Consulting Services Pvt Ltd2,005 open roles

Where
Remote
Work mode
Remote
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Your applicationOpen nowArchitect and Project ManagerPradeepIT Consulting Services Pvt Ltd · Remote

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: first seen 3 days ago

The posting

Architect and Project Manager

End Date: July 2025

· Scope:

o Migration Assurance Package

o Conversion and Unit Testing

· Databricks will oversee CT teams' conversion, re-conversion, and testing from predetermined data/code snapshot and validate that syntax compiles successfully in Azure Databricks in the DEV environment/workspace.

· Assist in the resolution of any issues arising from conversion and unit testing.

· Re-configuration/conversion of remaining xx jobs as needed

· DXC to retrieve remaining files from SFTP server

Data Validation Testing

· Oversee the execution of data validation testing using standard data validation hierarchy (row counts, column-level checks, row-level checks, data subset checks) with a DEV data set (or Production snapshot) in a QA environment/workspace.

· Databricks will assist in the review and resolution of common or recurring issues related to testing

Integration & User Acceptance testing

· Oversee the execution of End-to-End integration testing, which is defined as a functional data validation through the execution of Power BI/AAS reports and the comparison of data outputs via Power BI

· Databricks will assist in triage and resolution of issues during integration testing.

Parallel Runs for production readiness

· Advise client when promoting code to higher environments following standard Customer production process using their Continuous Deployment pipelines with manual trigger.

· Advise client on validation and production performance optimization best practices while the source system and Azure Databricks have been run in parallel on production data for up to six weeks.

· Support for parallel runs includes validation of report output prior to and post migration and performing data quality checks based on row counts, column-level checks, row-level checks, data subset checks. Parallel runs will be conducted by the Customer team with Databricks providing support for resolution of any issues.

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