Amarnath jatte

Requirement Elicitation
• Maintain contact with stakeholders, include stakeholders, and collect business requirements to document in BRD. By utilizing the
technical stack and meeting their requests and expectations.
• Handled data from different sources at various levels, gaining a thorough grasp of the data and its business context. Worked
independently and responsibly, demonstrating end to end management from ideas to final delivery.
Data Integration and Sync Process
• Manage the data warehouse for the enterprise’s data hub, build optimized and scalable ETL pipelines that can extract data from
various sources, transform it, and load it into the DataMart’s.
• Data pipeline processes were implemented for batch processing. We synchronized processes, used AWS S3 to extract and transform
data in Talend in accordance with business logic, and loaded that data into the production environment, which is Redshift.
Technologies used: AWS-Redshift, S3, Talend, SQL server, pandas.
Data Quality and Dashboard Building
• Implemented several data monitoring checks at granular levels to optimize efficiency to boost data integrity and data-driven
decision-making with high-quality data. Performed data quality assessment weekly, monthly at deep dive level to investigate the
breaks in data quality.
• Published on power-bi server user-friendly dashboard for data visualization that allows users to obtain summary reports at several
granular levels for weekly, monthly, and quarterly reports. Delivering requested data on an ad-hoc basis or using Power BI to create
detailed dashboards and reports that are interactive and aesthetically pleasing.
PROCESS IMPROVEMENT
KANTAR,Bangalore,India July’21–Oct’22
• An ETL process incorporates automation into the management of very large quantities of data sources. Python was used to create an
efficient ETL technique. As a result, we save 20% of our time on manual work.
• Created a fully streamlined real-time data visualization utilizing power-bI, SQL, ETL to analyze business analytics and needs at various
granular levels.
• There are several data sources with a big quantity of difficult to understand data sets. To grasp an ETL job’s data flowchart and
underlying business logic, all information documented.

Maintain contact with stakeholders and collect business requirements to document in BRD.
Handled data from different sources at various levels and gained a thorough grasp of the data and its business context.
Worked independently and responsibly, demonstrating end to end management from ideas to final delivery.
Manage the data warehouse for the enterprise’s data hub.
Build optimized and scalable ETL pipelines that extract data from various sources, transform it, and load it into the DataMart’s.
Implemented data pipeline processes for batch processing.
Used AWS S3 to extract and transform data in Talend in accordance with business logic.
Loaded data into the production environment, which is Redshift.
Implemented several data monitoring checks at granular levels to optimize efficiency and boost data integrity and data-driven decision-making.
Performed data quality assessment weekly and monthly at deep dive level to investigate breaks in data quality.
Published user-friendly dashboards on Power BI server for data visualization.
Delivered requested data on an ad-hoc basis.
Created detailed dashboards and reports that are interactive and aesthetically pleasing.
Used Python to create an efficient ETL technique.
Saved 20% of time on manual work.
Created a fully streamlined real-time data visualization utilizing Power BI, SQL, and ETL.
Documented information to grasp an ETL job’s data flowchart and underlying business logic.

An ETL process incorporates automation into the management of very large quantities of data sources.
Python was used to create an efficient ETL technique.
Saved 20% of time on manual work.
Created a fully streamlined real-time data visualization utilizing Power BI, SQL, and ETL to analyze business analytics and needs at various granular levels.
Documented all information to grasp an ETL job’s data flowchart and underlying business logic.