Subhadip Mukherjee

● End client - Best Buy.

● Responsible for designing and enhancing or developing warehouse components related to the Supply Chain Decision Support team and DDO merchandise team along with mentoring leading other team members. ● Designed and developed warehouse components in aws for sources. ● Some business segments are moving from Teradata to GCP Bigquery. Responsible for analyzing data and providing solutions in gcp with equivalent business functionality. ● Technology Used: GCP services. Database-Teradata. ETL-Informatica, Aleryx and Apache Nifi.

● Responsible for designing and enhancing or developing warehouse components related to the DDO merchandise team along with mentoring leading other team members. ● Build various services in GCP to support Data Development Operation. ● Lead an offshore team for the collaborative development works. ● Technology Used: Cloud stack- GCP, OnPrem - Informatica(Powercenter, Data Quality and observability, MDM), Apache NiFi, Teradata.

● Responsible for building the entire pipeline from source to reporting in GCP. 3 ● Migrated entire customer data from AWS MySQL RDS to GCP Firestore using python script. ● Entire analytical solution has been built on GCP using various GCP services like Firestore(Firebase), Cloud Storage, Bigquery, Data Flow, Data Transfer Service(DTS), Cloud Run, Cloud Functions, Pub-Sub, Firestore, Cloud SQL, Cloud Build, Airflow(For orchestration), Apache NiFi etc. ● Built ETL flows using DBT: ○ Created multiple models - Table/View/Incremental materialization. Used multiple incremental strategy as well(merge/append etc) ○ Integrated DBT models into Apache Airflow(GCP composer) for orchestration. ○ Generated DBT Documentation and served them. ● Built reporting for the business users in the Google Data Studio from the mart tables created as part of the analytical flow.

● Built Machine learning model to detect frauds in mobility subscription. ● Requirement gathering, understanding data, engineering features for the data science model, building the model and tuning the hyperparameters and automating the process flow is the key role as MLE/DE. ● Technology used - Rapidminer(During POC), Python [PySpark and SciKit-Learn] and MSSQL Server, Alteryx(To explore ETL functionality), Tableau(demonstrate ML output to management)