Himanshu S.

• Created a Data Warehouse solution utilizing AWS Redshift and AWS Glue, migrating an OLAP database from MS SQL Server.
• Established a DBT pipeline for ETL processes, transferring data between a MySQL warehouse and an activity database to a Neo4j graph database using native Python programming. The setup was implemented on an AWS Linux box with Neo4j running as a Docker container.
• Developed an ETL pipeline for conducting market basket analysis and other marketing statistics on millions of rows of transactional data. Utilized Redshift as a transactional database and populated it in a serverless fashion using Amazon Lambda functions in real time.

• Created a Data Warehouse solution utilizing AWS Redshift and AWS Glue, migrating an OLAP database from MSSQL Server.

• Established a DBT pipeline for ETL processes, transferring data between MySQL warehouse and activity database to Neo4j graph database using native Python programming. Setup was implemented on an AWS Linux box with Neo4j running as a Docker container.

• Developed an ETL pipeline for conducting market basket analysis and other marketing statistics on millions of rows of transactional data. Utilized Redshift as a transactional database and populated it in a serverless fashion using Amazon Lambda function in real-time.

Contributed in building Market Intelligence dashboard pipeline. Using unstructured review text, did a NER and relationship extraction to get Sentiment at entity level.

Trend forecasting and sentiment calculation to help businesses make better decisions and improve marketing strategy. Used AWS for cloud computing.

• Developed a pipeline to convert data into a structured format, enabling serving to Prodigy for ML-related tagging. The entire pipeline was constructed in a modular fashion using pure Python and shell scripting.
• Implemented data transformations in Python and stored the processed data in an Amazon S3 bucket for storage and accessibility.

• Automated the process of writing Hive queries for ETL of multiple tables (both one-time and incremental) by generating automated scripts.
• Read CSV files from folder locations, created tables, and performed incremental loads sequentially.
• Set up Snowflake as the primary storage solution for structured data and utilized DBT for ETL processes. Crafted SQL-based models to define transformation logic, ensuring flexibility with incremental loading and version control using DBT.
• Prepared transformed data for analysis using business intelligence tools, facilitating effortless insights discovery. Conducted regular checks in Snowflake and DBT to maintain data integrity and pipeline functionality.
• Designed and developed data pipelines to extract, transform, and load data from diverse sources into a centralized data warehouse.