I build data platforms, analytics and AI that whole companies rely on, and I lead the engineers who run them. A long career built in layers: software, spatial analytics, data platforms, then AI that answers business questions straight from the warehouse.
At a glance2004 – now · Cairo, working with Denver
Data platforms, analytics and AI, built hands-on
Data Engineering and Analytics
Long experience in software development, data platforms, analytics engineering and machine learning, with a background in spatial analytics. I moved from lead and staff engineer to manager to director, and I still design, code and ship.
It started with software and spatial solutions at Esri and Link Development. At Raisa Energy I built the data platform, the team behind it, and today AI that answers business questions from the warehouse, with spatial analytics still part of the toolkit.
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012004 · Cairo
Computer science foundations
Bachelor of Computer Science
I studied computer science at Cairo University1 and graduated in 2004.
In October 2005 I joined Esri Northeast Africa2, building GIS and spatial solutions.
Receipts · 2
1Faculty of Computers and Artificial Intelligence · 2004
2From October 2005 · Cairo, Egypt
022005 – 2015 · Esri Northeast Africa
Building spatial solutions
Technical Lead / Senior Developer
At Esri I designed and built GIS and spatial systems for clients in several countries, among them vehicle tracking for a municipal fleet3 and a spatial homeland security system4.
I led a group of engineers5 across front end, back end and spatial data analysis, and ran the work in Agile Scrum.
Receipts · 3
3A municipal fleet of hundreds of vehicles
4Geographical Security System (GSS) · a national government client
5Engineering group · front end, back end, spatial analysis · Agile Scrum
032015 – 2018 · Link Development
From location to analytics
GIS Development Manager
At Link Development I owned the technical direction of GIS development, and started combining spatial data analysis with machine learning6, so location became one more signal in the models.
I mentored a direct report7 and coordinated with engineers in other teams.
Receipts · 2
6Spatial analytics · clustering models · chatbots
7One direct report · Nov 2015 – May 2018
042018 – 2021 · Raisa Energy
Data platform and machine learning
Staff Data Scientist (Data Engineering and Machine Learning)
In 2018 I joined Raisa Energy, working with teams in Cairo and Denver. I kept spatial analytics in my toolkit and added machine learning, building classical machine learning models8.
And the platform underneath them: I ran a hands-on evaluation of Snowflake against Databricks, chose Snowflake, and introduced Snowflake and dbt9 to the company. I designed the full data platform, built ETL / ELT pipelines for the data science team, and wrote a framework that deploys a model with a simple script10.
Receipts · 3
8Regression, classification, clustering · well operating cost, landowner record deduplication, operator assignment
9Chosen over Databricks: SQL-first analysts, mostly structured data, low operating effort for a small team
10Logs features and predictions in production
052022 · Raisa Energy
Building the team
Data Engineering Manager
In 2022 I was promoted to build Raisa's first dedicated data engineering team11. I hired and onboarded the engineers, and we built the Snowflake data warehouse12 with pipelines, tools and dashboards for the different teams.
Receipts · 2
11Separated from the combined data science and engineering team
12Jan – Dec 2022
062023 – now · Raisa Energy
Leading data engineering
Director of Data Engineering
As Director I lead a team of up to seven engineers13, a team I built from scratch, and still ship hands-on in architecture, design and code. I own hiring, compensation and the team budget, and shape company strategy, roadmap and OKRs with the leadership team, working across time zones with the Denver team.
The warehouse is built in layers14 and brings several data providers, partner billing and accounting15 into one place, used by several teams across the company.
Receipts · 3
13Contributes to company budget planning · Jan 2023 – present
14Staging → intermediate → data marts → BI · custom medallion · dimensional models
15Novi, Enverus, WellDatabase · EnergyLink billing · W Energy accounting
072023 – now · Raisa Energy
AI over the warehouse
Director of Data Engineering
I built an MCP server and semantic layer on Snowflake Cortex16, so AI assistants answer business questions straight from warehouse data. The company depends on it now.
Two reviews that took days now take hours: an automated audit of partner invoices17 and snapshots of reserves runs18.
I introduced regular R&D weeks19 where the team evaluates new technology, and built AI agents and LLM tools20 for reporting, text-to-SQL and document extraction.
Receipts · 5
16Single sign-on through Azure Active Directory · role-based access
17About 40 checks on every joint interest bill · for the reserves and subsurface teams
18ARIES reserves and economics runs · compare any two · used company-wide
19First outcome: a Claude vs ChatGPT benchmark for writing SQL
20Moved from CrewAI to LangGraph for tighter control of agent flows · mentored an intern who built an agentic reporting tool
Raisa Energy · 2023 – now
Key deliverables
What I've delivered as Director of Data Engineering.
Team
Data engineering team
Built the team from scratch to up to seven engineers and still ship hands-on. Set standards for naming, modeling and documentation, review code and coach by asking questions, and run R&D weeks and knowledge-sharing sessions.
Result: Own hiring, compensation and the team budget
Hiring
Coaching
Standards
R&D weeks
Platform
Data warehouse and data layers
A layered Snowflake warehouse (staging, intermediate, marts, BI) with dimensional modeling. Shared core layers unify third-party data providers and join partner billing with accounting, and each team gets its own data mart.
Result: About ten source systems · used by several teams
Snowflake
dbt
SQL
Operations
Reliability, governance and cost
A config-driven framework loads only changed tables and keeps full history. dbt tests act as gates between layers, with role-based access, lineage, monitoring and cost tuning in Snowflake.
Result: Users keep the last good data when a test fails
dbt tests
SCD2
RBAC
Azure DevOps
Analytics
Analytics for decisions
Scoring models and indicators that turn engineering, financial and production data into one number. Example: the bounding score, built from nearby wells, their distance, cost and production, shows how surrounded each well is and how much production is at risk when new wells are drilled close by. It is used to approve or reject funding for new wells. Model runs are snapshotted and compared on one dashboard.
Result: Model-run comparisons days → hours
Python
Snowflake
Spatial analysis
AI
AI on top of the warehouse
An MCP server and semantic layer on Snowflake Cortex with Azure AD sign-on and role-based access. AI agents handle reporting, text-to-SQL and document extraction, and an AI-assisted audit runs about 40 checks on every partner invoice, with fixed checks in Snowflake and AI only where judgment is needed.
Result: Company-wide daily data questions · invoice review days → hours
MCP
Cortex
LangGraph
AI agents
Skills
Skills and tools
From leading teams to building data platforms and AI tooling.
Leadership and management
Team building and hiring, coaching and mentoring, compensation and budget, strategy, roadmap and OKRs/KPIs, technical product management, stakeholder alignment, remote work across time zones.