Younes Hamra

• Architected and automated large-scale data pipelines processing millions of events daily, enabling realtime ingestion, transformation, and analytics while improving data reliability and reducing operational overhead by 30%. • Built event-driven architectures using asynchronous messaging (Pub/Sub) to enhance system scalability and resilience, supporting 24/7 high-availability distributed environments. • Optimized storage and query performance for multi-terabyte datasets in BigQuery, enabling fast and cost-effective analytics. • Deployed containerized workloads and serverless applications on GCP (Cloud Run), accelerating delivery cycles and enabling robust CI/CD pipelines. • Implemented automated workflow orchestration via Cloud Composer, Cloud Scheduler, and Workflows, reducing manual intervention and boosting operational efficiency. • Secured application and data access using Google Secret Manager, ensuring compliance with enterprise security policies. • Streamlined build, deployment, and monitoring processes using GitLab CI/CD, Cloud Build, and Cloud Monitoring, achieving 20% less downtime and more proactive incident management. • Led cross-functional data workflows integrating analytics, transformation (dbt), and orchestration to deliver scalable and actionable insights at enterprise scale. Tech stack: GCP (Composer, Dataflow, BigQuery, Pub/Sub, Cloud Run, Compute Engine, Cloud Storage, Cloud Functions, Secret Manager, Cloud Scheduler, Artifact Registry, Workflows, Cloud Build), Terraform, GitLab CI/CD, Docker, Python, dbt

Developed and implemented a scalable ETL pipeline using Spark with Scala and AWS services (Step Functions, Glue, Lambda, Athena, S3) for efficient data processing and system integration. Designed and orchestrated automated ingestion and transformation workflows across heterogeneous data sources, ensuring data quality and consistency. Optimized Spark job performance and leveraged serverless AWS architectures to improve cost-efficiency and resource utilization. Built fault-tolerant, highly available data pipelines guaranteeing data durability and minimal downtime. Collaborated with cross-functional teams (data analysts, engineers, business stakeholders) to translate business requirements into tailored technical solutions within the pharmaceutical industry. Implemented monitoring and alerting frameworks to ensure pipeline health, data integrity, and rapid incident resolution. Technical Environment Spark (Scala), AWS Step Functions, AWS Glue, AWS Lambda, AWS Athena, AWS S3, CloudWatch, Python, GitLab

Designed and implemented both streaming and batch ETL pipelines using Spark (Scala/Python), Kafka, and various storage systems (CockroachDB, Hive, Elasticsearch), enabling real-time and large-scale data processing. Developed custom monitoring and observability dashboards in Grafana and Kibana, improving visibility and system reliability. Optimized Spark Streaming jobs and SQL transformations, reducing latency and improving throughput for mission-critical pipelines. Automated workflow orchestration and scheduling with tools such as Oozie and Streamsets, ensuring reliable and scalable data ingestion. Applied machine learning models on big data platforms to generate predictive insights, including data exploration, feature engineering, and model evaluation at scale. Built data-driven Proof-of-Concepts to assess new architectures and technologies (real-time ingestion, KPI computation, search and indexing systems). Designed and deployed a CI/CD-enabled data platform, integrating Dockerized microservices, back-end development (Python/Django), and continuous deployment pipelines from GitLab to Azure. Collaborated closely with cross-functional teams (Data Scientists, Developers, Business Analysts) to design end-to-end solutions from ingestion to analytics. Technical Environment Spark, Kafka, Hive, CockroachDB, Elasticsearch, Grafana, Kibana, Streamsets, Oozie, Python, Docker, GitLab CI/CD, Azure