Mokhtar Riahi

Data Cloud Engineer

BMW Group • Temps plein

Jun 2024 - Present • 1 yr 11 mos

• Manage and optimize AWS infrastructure for cloud data workloads, with a focus on automation, scalability, and cost efficiency using Terraform. • Designed and implemented the Cloud Efficiency Analytics (CLEA) platform, aggregating multi‑account cloud data to generate optimization scores and actionable cost recommendations. • Designed end‑to‑end ETL/ELT data flows across Amazon S3, AWS Glue, and Step Functions, exposing curated insights to stakeholders through Amazon QuickSight dashboards. • Built and operated production data pipelines using AWS Glue, Step Functions, Lambda, SQL, and dbt. • Integrated Azure APIs and user interfaces with AWS‑based workflows and implemented spend‑threshold alerting to improve operational visibility. • Modeled analytics‑ready datasets to support dashboarding, KPI tracking, and automated reporting. • Implemented CI/CD deployment workflows with GitHub Actions to improve delivery consistency and deployment automation • Built AI‑powered reporting pipelines by integrating industrial datasets from Athena and SiteWise with AWS Bedrock to automate insight generation. • Developed serverless workflows with Lambda and Step Functions to orchestrate LLM‑driven report generation and automated delivery to end users. • Translated complex industrial data into business‑ready insights through LLM‑generated summaries for non‑technical stakeholders. • Architected and implemented industrial data platforms using HighByte and Litmus for real‑time data modeling, integration, and edge connectivity. • Designed and deployed scalable AWS data pipelines enabling reliable ingestion, transformation, and analytics of industrial telemetry data. • Contributed to technical proposals, white papers, and RFPs, shaping solution architecture for enterprise opportunities. • Delivered presentations to internal teams and clients on cloud architectures, data platforms, and AI‑driven analytics solutions