Teradata Vantage, Azure (VM, Storage Account, Data Factory – ETL/Data integration service, Batch Service)

On-prem to Cloud full scale data migration. Using the Azure data factory as the main driver for migration in which TPT jobs were created through Azure batch service.

Teradata Vantage and Azure (VM, Storage Account, Event Hub – Data streaming platform and event ingestion service)

Helped the presales architect to prepare and execute test build or benchmark queries using TD Bench. Created reusable assets (documentation, scripts/programs, etc.) from project artifacts. Assisted sales in positioning Teradata in Azure database offerings.

Teradata Vantage, Aster

Responsible for building and executing test cases for each application as applicable. Facilitating the Unit Testing, SIT and UAT process. Setting-up communication between users/owners, development team and testing team. Initiating RCA in the event defect is a showstopper. Responsible for issues and defect management. Requirement Analysis (sources: documents, scripts, emails and meetings). Owning the database objects catalog (tables, views, Aster functions, etc. that have been identified during the requirements analysis). Working with Aster to Vantage DDL conversion. Working with SQL conversion between Aster and Vantage (Native SQL, SAS scripts, Shell scripts, etc.). Including those Aster functions (i.e. LARS, LARSPREDICT, GLM, GLMPREDICT, SESSIONIZE, etc.)

Python, Jupyter, Excel VBA, Postgres DB

Data Governance, Data Quality Assessment and Data Profiling. Led the implementation of the physical architecture of the data quality loop process and created a tool for data quality rules maintenance. Helped the Solution Architect and Project Manager to the following tasks: Reviewed the findings on data quality measures and processes. Conducted detailed assessment of the readiness of the company to digest new quality procedures (Verify CRUD process compatibility). Conducted on the completeness of the required inputs from company to ensure team’s readiness Conducted on company’s existing data quality process and quality rules before proceeding with the activity to define, design and implement. Collected data quality rules. Identified the attributes and subject areas to be included in this project. Created a prioritized list of data tables and attributes. Collected and defined associated KPI's. Data quality process/profiling review. Reviewed the project findings on data quality measures and data quality processes.