Omar Mohamed Hussein

An advanced 3-month mentorship programme for experienced Data Engineers and aspiring Platform Architects and Team Leads who want to design and own full data platforms — not just pipelines. Based on real national-scale lakehouse deployments across healthcare, government, real estate, and energy, this programme covers platform architecture, streaming at scale, AI/ML enablement, governance, and the technical leadership skills required for senior and staff-level roles. What's included: End-to-end data platform architecture and technology selection Designing an open-source lakehouse (Delta Lake + Spark + Kafka + Airflow) Real-time streaming architecture and exactly-once processing patterns Feature Store design for offline training and online inference ML lifecycle enablement: model serving with FastAPI, drift detection, MLOps Data governance: lineage, catalog, audit trails, and regulatory compliance Zero Trust security for data platforms: RBAC/ABAC, column & row-level protection Multi-tenant and sector-agnostic platform design Scaling, capacity planning, and cost governance Incident response and reliability for production data systems Healthcare data integration deep dive (HL7, FHIR, HIS/LIS/RIS) — optional track Technical leadership: leading data teams, setting standards, mentoring engineers Architecture design reviews on your real production systems Senior / Staff Data Engineer and Architect interview preparation Personalised career roadmap Best for: Mid Level Professionals · Senior Professionals · Data Engineers · Engineering Leads · Job Seekers

A comprehensive 3-month mentorship program for Data Engineers, BI developers, and backend engineers moving into data. Built around real enterprise projects, this program covers distributed processing with Spark, real-time streaming with Kafka, and the Bronze/Silver/Gold lakehouse architecture used in production data platforms — with the depth required for mid-level Data Engineering roles. What's included: Apache Spark: DataFrames, partitioning, joins, and performance tuning Delta Lake and the Bronze / Silver / Gold medallion architecture ACID transactions, schema evolution, and time travel in practice Apache Kafka fundamentals and Spark Structured Streaming pipelines Airflow DAG design, dependencies, retries, and production scheduling DBT for transformation, testing, and documentation Data modeling for analytics at scale Data quality frameworks, validation, and pipeline monitoring Cloud platforms: AWS / Azure / Databricks essentials for data engineers Docker and CI/CD for data pipelines Cost and performance optimisation of large workloads Code and architecture design reviews on your own work Data Engineering interview preparation (SQL, Spark, system design) Personalised career roadmap Best for: Junior Professionals · Mid Level Professionals · Job Seekers · Anyone Looking To Grow Their Career

A practical 1-month mentorship program for aspiring Data Engineers, analysts, and CS graduates who want to build a solid foundation in modern data engineering. Learn how data moves from source systems to analytics-ready tables, write production-quality SQL and Python, and build your first real ELT pipeline using the same practices used in enterprise data teams. What's included: Data engineering landscape: OLTP vs OLAP, warehouse vs lake vs lakehouse Advanced SQL for data engineers (window functions, CTEs, query tuning) Python for data engineering: pandas, APIs, file formats (Parquet, CSV, JSON) Relational modeling fundamentals: star schema, fact & dimension tables Building your first ELT pipeline end-to-end Introduction to Apache Airflow and workflow orchestration Git, environments, and engineering hygiene for data projects Cloud data warehouse basics (BigQuery / Snowflake / Redshift) One guided portfolio project you can put on GitHub Resume and LinkedIn profile review Personalised learning roadmap Q&A support between sessions Best for: University Students · Fresh Graduates · Interns · Junior Professionals · Entry Level Professionals