Divya Dass

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

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

<p>Led a Project that included a Data Pipeline consisting of Databricks and Snowflake.<br>
Led a Snowflake PoC as part of the PreSales team for an online jeweler.<br>
Contributed to the Optimization cycle of various data projects.</p>

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

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

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

<p>Designing Dimensional Datamart.<br>
Transform data at S3 via Glue.<br>
Creating a View in Redshift over the fact table.<br>
Output Table design for meeting requirements for daily grain data.<br>
Transform data at S3 via Glue job.</p>

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

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