Issame H.

Context: Within the Cloud & Cybersecurity division, design, deployment, and commissioning of secure, sovereign, high-performing, and optimal infrastructures (OnPrem/Azure), enabling business teams, Data Engineers, and Data Scientists to easily develop and deliver dashboards, data pipelines, and ML models with improved performance.

Achievements:

-Wrote technical architecture documents (DAT) for several projects, in collaboration with application, system, and network architects.

-Developed and automated the provisioning of Azure infrastructures using Terraform and Ansible (VMs, Vnet/Subnet, Service/Private Endpoints, NSG, ASG, Application Gateway, WAF, RBAC, SPs, Groups, Users, Databricks, Synapse, Azure SQL, PostgreSQL, Eventhub, Azure function, Keyvault, Storage account, AKS…)

-Developed, optimized, and tuned several Databricks (Spark) data pipelines.

-Implemented a monitoring solution for all SI Databricks using Datadog, Eventhub, an Azure function, and a log4j v2 logger.

-Implemented CI/CD pipelines using Jenkins.

-Implemented Rundeck orchestration jobs.

-Wrote operational documents (DEX).

-Conducted client demonstrations.

-Supported Data, ML, and business teams.

Tools: Azure, Databricks, Apache Spark, AdlsGen2, Java/Scala/Python, Docker, Kubernetes, Helm, Jenkins, Ansible, Terraform, Datadog, Rundeck, SQL Server, Synapse…

Context: Within the Data teams and in collaboration with Data and Network architects, defined the Data strategy (data valorization, data acculturation as a product, governance…) and designed several tools for the new Data platform.

Achievements:

-Ingestion and transformation of data in the datalake: controls, repositories…

-Designed and implemented a data quality verification tool.

-Designed and implemented a sensitive data anonymization tool.

-Implemented a Job API that centralizes Spark job launches on AKS.

-Installed and secured a Zeppelin datalabe (Spark interpreter).

-Developed end-to-end Data pipelines (Apache Spark) (ingestion, transformation) meeting various business use cases.

-Designed a Self-service tool allowing business users to easily exploit the Datalake (Drag & Drop).

-Implemented a metrics collection and display layer with Prometheus and Grafana.

-Implemented a logging layer with Loki and FinOps with Kubecost.

-Participated in projects to improve data-related tools and processes within the DDC (requirements gathering, development of appropriate tools or interfaces with internal or external IT, acceptance testing, development of user materials and training).

-Provisioned Azure infrastructures with Terraform and Ansible.

Tools: SAFe, Azure (AKS, ACR, Cosmos, SQL Server, Event Hub, DataFactory…), Apache Spark, Java, Spring Boot, Maven, Deequ, Docker, Harbor, Azure Devops, Ansible, Helm, Terraform, Prometheus, Loki, KubeCost, Grafana, Keycloak…

Context:

Implemented an end-to-end Big-Data Project: from data ingestion to restitution, to improve fraud detection.

Migrated all applications (frontend, backend, big-data) from On-Premise to Cloud Azure.

Achievements:

- Ingestion and transformation of data in the datalake: controls, repositories...

- Designed and implemented several data ingestion/transformation modules from various sources (Oracle, XML, JSON, CSV, APIs...)

- Exposed datalake data via Hive tables/views.

- Designed and implemented a tool (frontend, backend) for querying and exporting data from the datalake.

- Designed and implemented a data indexing solution: Elasticsearch, HBase.

- Implemented continuous integration/deployment workflows with Ansible, Jenkins, GIT, Nexus, Oozie.

- Migrated all modules (frontend, backend, bigdata) from on-premise to Cloud Azure with: HDInsight, ADLS GenV2, AKS, Kubernetes, Docker-compose, Harbor, ArgoCD, Helm, ...

Stack: Hortonworks, Spark, HDFS, Hive, ELK, Oozie, kerberos, Spring boot, Git, Jenkins, Nexus, Ansible, Cloud Azure, ADLS GenV2, Airflow, Harbor, HDInsight, AKS, Docker