À propos de Mohammed
I am a skilled and experienced Data Engineer with a proven track record of implementing and integrating Data solutions to customers analytics ecosystem.
My expertise extends to cloud-based solutions, where I have successfully migrated legacy workflows to modern Lakehouse architecture using Databricks or Palantir Foundry
My commitment to staying current with industry trends and my passion for leveraging innovative technologies make me an asset in driving data-driven decision-making within your organization
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Arabe
Bilingue ou natif
Accepte de travailler sur site
Paris (jusqu’à 50 km)
Expériences
- Databricks, Palantir, AwsLead techoctobre 2024 - Aujourd'hui (1 an et 8 mois)Paris, France• Designed a Data architecture framework to collect and process Data coming from several entities (Americas, Europe...).• Ingested Data from different sources using Databricks API with a service principal authentication pattern.• Performed business level aggregations using scheduled PySpark jobs running in a distribution-based cluster.• Configured DBX API to allow eventful schema migration of data (coming from ERP and legacy warehouses) to DBX• Configured DBX CLI and DBX Connect to local IDE (VS code)• Synced AWS S3 files with DBX to allow ACID queries on external tables• Migrated Data pipelines from Palantir Foundry to DBX using PySpark• Administered both Databricks workspace and AWS Datalake organization of the business unit, setting up groups for granular access management (using STS assumed roles)• Successfully connected Databricks SQL warehouse to PowerBI desktop in import mode• Collaborated with local security officer to plan and assess security and confidentiality levels of Data to ensure compliance
- SCORPalantir specialistBANQUE & ASSURANCESjanvier 2024 - Aujourd'hui (2 ans et 5 mois)Paris, France
- Led the deployment and integration of Palantir Foundry with Databricks on Azure. ensuring seamless data flow across plateforms and implementing Data quality checks for job monitoring
- Developped and configured ontology object types in a production environment, utilizing Foundry capabilities to create unified data products that improves discoverability, tracibily and governance.
- Created an operational dashboard fo workforce management using workshop, leveraging ontology features such as rules, actions and scenarios along with custom Typescript functions to provide real time insight into operational workforce management
- Designed and implemented ontology-based AI models leveraging AIP to create context-aware solutions.
- Databricks, AzureData Engineermai 2023 - septembre 2024 (1 an et 4 mois)Paris, France• Designed and developed high performance ETL pipelines using Pyspark and Delta Lake• Collaborated in developing a Pyspark engine to connect and extract Data from SQL server in an append mode.• Deployed pipelines in production environments• Monitored pipelines performance and implemented optimization technics (Partitioning, Broadcasts, shuffles) to handle more volumes of Data• Ensured Data quality by developing custom Data expectation scripts to track Data freshness and completeness• Configured job schedules using Apache Airflow and built in Databricks orchestrator• Collaborated on code with 5 other Data engineers using Azure Devops versioning tool• Object oriented programming and TDD in DBX using PyTest Library
Recommandations
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Formations
- Master of Advanced StudyISAE-SUPAERO2019Advanced Master
- Engineer's degree, Industrialization- production- automatizationÉcole Nationale Supérieure d'Arts et2018Engineer's degree, Industrialization- production- automatization