Adel Gasmi

Project 1: DOMO platform admin and owner of a plethora of data projects: Designed and maintained ETL pipelines for data integration & KPIs calculation of 5 use cases using RedShift/MagicETL/MySQL engines (technical specs, data modeling, and data transformation logic) Mentored junior developers Led data projects maintenance and data governance (platform used by 3,000 users) Project 2: Migration from legacy BI tools to GCP/PowerBI/Looker stack Elaborated functional/technical specifications for 5 use cases Contributed in building the new group data warehouse (BigQuery) using Airflow/dbt/Dataflow/Cloud Functions/Workflows and other services Developed different data flows and data models using Looker/PowerBI handling 100s of TBs of Social Media and Google Analytics data Use new optimization techniques to decrease loading time by 10x for PBI dashboards in directquery mode

Product Owner of 4 data projects as a project manager (MS BI, SSAS, SSIS, Neo4j Graph DB and other proprietary solutions) Validated proposed architectures and ensured compliance with security rules Negotiated with data solutions vendors and challenged them on cost, features, efficiency and maintainability Supported live projects

Project 1: Cloud Strategy for Insurance (6 months): Led IFRS17 data pipeline setup, migrating data from on-prem to GCP of a total of 10 TB of data Helped to deploy a Dataiku cluster on GKE Put in place a guide on data modeling and transforming SAS scripts into SQL/Python scripts in Dataiku pipelines Deployed a Spark cluster over GKE for advanced federated calculations, testing it yielded more efficient jobs for advanced calculations Project 2: Systems Integration for Big Pharma (12 months): Designed functional and technical specs for the different interfaces integrating SAP into Coupa Developed ETLs with Informatica IICS. Built a monitoring dashboard using Kafka, Splunk for the different jobs and interfaces Conducted testing, training, and Level 3 support Project 3: Analytics for Retail (6 months): Managed Databricks cluster on Azure for the use case Developed efficient ETL (Bronze/Silver/Golden) processes cutting down costs by 60% Designed classification/clustering models, deployed with MLFlow optimizing promotion offer for retailers performance by +30%

Developed a monitoring tool (REST API) for airplane seatmap management for more than 150 airline companies Implemented real-time data processing pipeline using Java and Kafka Applied clustering/classification techniques on offline data (Lambda Architecture) to provide the clients recommended seat maps based on efficiency, cost, filling rates, and profitability using Databricks Spark Scala MLLib

The project consisted of creating a crime analysis platform developed in Django/Js Utilized machine learning techniques (clustering, classification using sklearn) for crime modeling, mapping and classification Developed and benchmarked a composite model for crime forecasting aggregating and selecting the champion model among different ones (LSTM, ARIMA and FbProphet) Designed big data architecture using Apache Spark Scala for offline data analysis