I build pipelines that move real data reliably — batch and streaming — and the CI/CD and infrastructure around them so they keep running without babysitting. From raw events to a dashboard someone can actually use.
I'm a Computer Science & AI graduate (Menoufia National University, Jun 2026, GPA 3.4) focused on data engineering and DevOps. My work sits at the practical end: pipelines in Python, PySpark, Kafka, Flink and Airflow, containerized with Docker, deployed on AWS, and orchestrated so failures are retried and visible — not silent.
Alongside pipelines, I do data analysis and web scraping: cleaning messy source data into structured datasets, and turning it into reports and dashboards that answer a business question, not just a chart.
I also build rhash — hands-on learning tracks for Terraform and Go, written from official docs with in-browser labs.
ETL and streaming pipelines from ingestion to a queryable store: Kafka ingestion, PySpark / Flink processing, partitioned data lakes, and star-schema models in PostgreSQL.
Docker and Docker Compose deployments, GitHub Actions pipelines with artifact packaging and versioned releases, and automated deploys to AWS Elastic Beanstalk — with scheduling, retries, and cluster health in mind.
Web scraping into clean, structured datasets (10,000+ e-commerce product records), data cleaning with Power Query and Pandas, and Power BI dashboards for stakeholder reporting.
Open source, on GitHub. Each one is a working pipeline, not a screenshot.
Fault-tolerant streaming on AWS: a KRaft-mode Kafka broker on EC2 for decoupled ingestion, PySpark Structured Streaming processing 5K events/min into a partitioned S3 data lake in 10-second micro-batches.
CI/CD for Veriixa, an AI-powered verification platform (graduation project): automated Maven builds with artifact packaging on every push and pull request to main.
A daily 4-task Airflow DAG that ingests, cleanses, and loads 950K+ data points from Johns Hopkins datasets into a star-schema PostgreSQL model — fully containerized with Docker.
Kafka to HDFS with Apache Flink in Docker, with automated Hadoop path integration to streamline cluster configuration — checkpoint and recovery to keep data consistent across restarts.
View on GitHub → ETLA PySpark ETL pipeline that flattens deeply nested JSON into a relational schema, loading transformed records into PostgreSQL with native array-type support — no manual schema definitions.
View on GitHub →Cleaning and analysis work: 10,000+ marketing records cleaned and transformed with Excel and Power Query, turned into 10 interactive Power BI dashboards surfacing key business metrics — plus e-commerce scraping into structured, analysis-ready datasets.
Ask me for a walkthrough →Bachelor of Computer Science & Artificial Intelligence, Jun 2026 — GPA 3.4 (Very Good), ranked 7th amongst colleagues.
End-to-end pipelines across the Hadoop ecosystem (HDFS, MapReduce, YARN, Hive, HBase) as part of Huawei HCIA-Big Data certification training. Batch and streaming with Spark, Flink, and Kafka, on Linux clusters.
Cleaned and transformed 10,000+ marketing records with Excel and Power Query, and built 10 interactive Power BI dashboards for stakeholder reporting.
Looking for a remote data engineering or DevOps role — or a freelance pipeline / scraping project. The fastest way to reach me is email.
[email protected] · github.com/Hesham942 · LinkedIn
Also on this site: rhash learning tracks — Terraform and Go, hands-on.