Senior Analytics Engineer · Data Engineer · Finance Data Analyst
I build data platforms that regulators trust and engineers enjoy maintaining.
Analytics Engineer with 5+ years. I spend most of my time in the space between raw data and a regulator's inbox — building pipelines that are automated, validated, and boring in the best way (no surprises at 3 AM).
Currently at Vision Bank (Saudi Arabia):
- Collaborating in PostgreSQL/Oracle → Google BigQuery migration with Google Professional Services
- Automating SAMA regulatory reporting end-to-end: schema discovery → dbt models → Airflow orchestration → validation frameworks → submission
- Building a Self-Service BI layer on Tableau so analysts stop asking me for ad-hoc queries
Previously shipped BI platforms at ZATCA (with Deloitte & PwC), TETCO, and Blueprint Technologies (Dubai).
Raw Source → Staging → Intermediate → Marts → BI / Regulatory Reports
│ │ │ │ │
│ dbt source dbt models Star Schema Tableau /
│ freshness + tests Kimball Looker /
│ checks + docs modelling Power BI
│ │ │ │ │
└──────── Airflow orchestrates the whole thing ────────────┘
│
BigQuery / Snowflake / Databricks
(the warehouse layer)
Opinions I hold:
- dbt tests are not optional — if it doesn't have
not_nullandunique, it's not production-ready - Dimensional modelling (Kimball) still wins for analytics — wide tables are a code smell
- Regulatory pipelines need reconciliation at every hop, not just at the end
- If a dashboard takes more than 10 seconds to load, it's an engineering failure
ETL Runtime 60+ min → 5–10 min ██████████████░░ 80% faster
Dashboard Refresh 2+ hours → under 5 min ████████████████ 95% faster
File Size 500 MB → under 100 MB ████████████░░░░ 79% smaller
Regulatory Accuracy ———————————————————————— ████████████████ 100%
Executive KPIs ———————————————————————— ████████████████ 100+ shipped
Data Engineering & Orchestration
Programming
BI & Visualisation
Cloud & DevOps
Regulated Domains
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learning_queue = {
"deep_dive": ["Apache Spark / PySpark", "Databricks", "Data Vault 2.0"],
"leveling_up": ["MLOps fundamentals", "Great Expectations (data quality)"],
"studying": "MSc Data Science — Rome Business School (2025–present)"
}| Project | What It Does | Stack |
|---|---|---|
| 🏦 regulatory-reporting-framework | End-to-end SAMA regulatory reporting: staging → transformation → validation → submission. Reconciliation checks at every hop. | dbt, Airflow, BigQuery, Python |
| 🧱 dbt-dimensional-model | Sample dimensional model (Star Schema / Kimball) with staging, intermediate, and mart layers. Includes dbt tests, docs, and CI. | dbt, BigQuery, GitLab CI |
| 🔄 airflow-orchestration-patterns | Production Airflow DAG patterns: sensor triggers, dynamic task mapping, failure alerting, SLA monitoring. | Python, Airflow, Docker |
| ⚡ sql-performance-cookbook | Advanced SQL optimisation: CTEs vs subqueries, window functions, partition pruning, query plan analysis. Real examples from BigQuery + PostgreSQL. | SQL, BigQuery, PostgreSQL |
| 📊 power-bi-optimization-toolkit | DAX refactoring patterns, data-model compression, incremental refresh configs. Cut dashboard refresh by 70–95%. | Power BI, DAX, SQL |
Some repos contain sanitised examples inspired by production patterns — no proprietary data.
🎓 MSc Data Science — Rome Business School, Italy (2025 – Present)
🎓 BSc Mathematics & Computer Science — University of Gezira, Sudan (2014 – 2020)
💬 I like talking about: dimensional modelling · dbt patterns · regulatory data pipelines · cloud migration war stories · why your dashboard is slow
📬 Reach me: mr.omarmoffed@gmail.com · LinkedIn · omaralfarouk.work