Divya Dass

Acted as the main SPOC for Data Projects from the PreSales team.
Requirement understanding.
Proposal creation.
RFP submission.
BOM creation.
Creating the fixed price commercial sheet for the Project.
Comparison between different hyperscalers.
PoCs.
Part of the Delivery team - Architect in Data Projects.
Understanding current architecture and requirements.
Creating to-be architecture while keeping cost and performance in mind.
Helped in increasing BigQuery and Snowflake expertise across the floor.
Led PoCs of batch and near-real-time pipelines in the cloud.
Used services and tech such as DataProc, Pub/Sub, Kafka, Snowflake, BigQuery, Redshift, Debezium, Airflow.
Served as a cross-functional technical specialist, architecting and optimizing solutions for high-priority project roadblocks.
Enhanced performance of a real-time data pipeline.
Optimized Power BI dashboards refresh time.
Re-architected a non-scalable scraping solution.
Extracted data from a locked legacy COBOL system.
Acted as the company’s representative in Snowflake and GCP events.
Involved in PreSales.
Architected event driven reporting architecture in-place of cron based system.
Lead Delivery of the Project.
Detailed Project Plan creation.
Work assignment to the Team.
Helping the team with technical challenges and decisions.
Making responsibilities clear among the team.
Main SPOC for Daily customer Standups.
Handling Customer Queries.
Leading Detailed discovery sessions when a new module gets picked up.
Involved in PreSales & Lead Delivery of the Project in the initial phase.
Lead Delivery of the Project in the initial phase.
Detailed Project Plan creation.
Work assignment to the Team.
Helping the team with technical challenges and decisions.
Making responsibilities clear among the team.
Main SPOC for Daily customer Standups.
Handling Customer Queries.
Leading more detailed discovery sessions when a new module gets picked up.
Lead Delivery of the Project.
Databricks was used to extract data from Sources and perform basic transformations.
Snowflake was used to store the existing and new datamart tables and power the Power BI dashboards.
The project’s aim was to reduce data freshness and enable Analytics at scale within the organization.
Work assignment to the Team.
Helping the team with technical challenges and decisions.
Making responsibilities clear among the team.
Main SPOC for Daily customer Standups.
Handling Customer Queries.
Customer communication to understand the current tech landscape, determine the scope of the Project, to estimate effort.
Prepare the “to-be” architecture in the destination Hyperscaler/OEM and creation of BOM.
Discussion and alignment with Hyperscaler and the customer’s team for the same.
PoC to benchmark the cost and performance of the suggested Architecture and other activities.
CDC-Based Ingestion Pipeline.
SQL-based Data Transformation and optimization in the new DwH.
Walkthrough of the suggested Data governance components.
Optimization of existing Spark Pipeline.
Creation of Dashboards in the new Proposed Visualization tool.
Demonstrating AI Capabilities in the Proposed Hyperscaler.
RFP Understanding and Submission.
Meeting the required SLAs in the PoC.
Reduce processing time by 50% with a 50% reduction in cost.
Reduction in Data freshness for the end Analytical user.
Optimize training time for the ML Model.
Optimize the response time of the Power BI dashboard.

a. Acted as the main SPOC for Data Projects from the PreSales team. Responsibilities - Requirement understanding, Proposalcreation, RFP submission, BOM creation, Creating the fixed price commercial sheet for the Project, comparison betweendifferent hyperscalers, PoCs.b. Part of the Delivery team - Architect in Data Projectsc. Understanding current architecture and requirements. Creating to-be architecture while keeping cost and performance inmind.d. Helped in increasing BigQuery and Snowflake expertise across the floore. Led PoCs of batch and near-real-time Pipelines in the Cloud. Used services and tech such as DataProc, Pub/Sub, Kafka,Snowflake, BigQuery, Redshift, Debezium, Airflow, etcf. Served as a cross-functional technical specialist, architecting and optimizing solutions for high-priority project roadblocks.E.g. Enhanced performance of a real-time data pipeline, Optimized Power BI dashboards refresh time, re-architected anon-scalable scraping solution, and extracted data from a locked legacy COBOL system.g. Acted as the company’s representative in Snowflake and GCP events

a. Led a Project that included a Data Pipeline consisting of Databricks and Snowflakeb. Led a Snowflake PoC as part of the PreSales team for an online jewelry. Contributed to the Optimization cycle of various data projects

Data extraction using SQL from Teradata.
Cleaning using Python.
Data representation in Tableau.
Publishing dashboards.
Refreshing dashboards on a monthly basis.
Discussion with Business SPOC and making new changes to dashboards as needed.
Data extraction from MongoDB using Python.
Writing Python analysis scripts.
Showcasing the results obtained to the team.

a. Root Cause Analysis Dashboard for Adjusted Claims:i. Data extraction using SQL from Teradata, cleaning using Pythonii. Data representation in Tableau, publishing dashboards, and refreshing them on a monthly basisiii. Discussion with Business SPOC and making new changes to dashboards as neededb. Publishing the Analysis report over the Model output datai. Data extraction(Model output) and preparation from MongoDB using Pythonii. Writing Python analysis scripts and showcasing the results obtained to the team

Forecasting demand for new products using data of similar products in previous years.
Generating recommendations of similar products using AWS Personalize.
Daily forecasting for the quantity of orders at hourly intervals for each store and for each item.
Model deployment at Sagemaker training job using AWS ECR, Lambda, and CloudWatch event.

a. Forecasting Demand for newly launched products (AWS): Forecasting demand for new products using data of similarproducts in previous years.b. Generating recommendations of similar products using AWS Personalize: Use interaction data between customers andproducts to generate recommendations via AWS Personalize.c. Sales forecasting for Quick Service Restaurant (AWS, QSR): Daily forecasting for the quantity of orders at hourly intervalsfor each store and for each item. Model deployment at Sagemaker training job using AWS ECR, Lambda, and CloudWatchevent.