D01 / Data capability
Modern data platforms, warehousing & BI
Operational and analytical data brought into durable models, semantic layers and decision-ready reporting.
Drawing EFF / DT–01 · Production data systems
Data platforms engineered as production systems.
Data is one of the deterministic engineering layers that dependable AI relies on. We design it for correctness, movement, scale and controlled evolution — not merely for storage.
01Production data engineering
Control the complete data boundary.
From source change to business use, every transition has to preserve meaning, integrity and operational control.
D02 / Data capability
Complex pipelines, synchronization & processing
Metadata-driven ETL/ELT, change capture, replication and high-performance processing across heterogeneous systems.
D03 / Data capability
Real-time & operational data systems
Data platforms engineered for distributed devices, high event volumes, low-latency supervision and operational action.
D04 / Data capability
Reliability, observability & controlled evolution
Validation, traceability, performance control and migration discipline for data systems that cannot stop or drift silently.
02Selected data systems
Four different data boundaries.
A deliberately narrow selection chosen for architectural range, operational consequence and evidence — not as a complete career history.
Hybrid Microsoft Fabric analytics platform
A metadata-driven architecture unifying data from up to ten operational databases through timestamp-based and change-tracked pipelines, a consolidated Lakehouse star schema and an incrementally refreshed Power BI semantic model.
Validated outcome
End-to-end proof of concept validated against its data-accuracy and 15-minute refresh acceptance criteria.
Near-real-time operational data at city scale
A 2 TB SQL Server platform connecting 3,000 distributed controllers with the operational, warehouse, reporting and analytical layers required for infrastructure supervision.
Production scale
More than 400 million events and measurements processed each year in near-real-time.
Zero-downtime production data-platform consolidation
A controlled migration that verified database code, views and data under production-equivalent load before consolidating a synchronized SQL Server estate.
Production outcome
Twelve servers consolidated to two with zero downtime and zero production incidents.
Data-product and monetization strategy
A strategic architecture for transforming transactional data into ML-ready time-series datasets, real-time insight products and commercially usable data services, with an explicit model-explainability framework.
Engagement boundary
Strategic architecture and phased technical roadmap — presented as advisory, not as a delivered implementation.
03Engineering assets
When the platform lacked control, we engineered it.
Reusable technical contributions that demonstrate depth below the architectural diagram — orchestration, traceability and synchronization implemented at the database boundary.
czAsyncSQLServer
Advanced asynchronous processing and orchestration inside SQL Server.
A Service Broker-based T-SQL system for grouped parallel jobs, nested dependencies, continuation steps, error propagation and execution-state tracking — created to make demanding database workloads controllable as well as fast.
- Parallel task groups
- Dependency-aware continuation
- Error & state tracking
SQL Server Git
Server-side traceability for database schema and code changes.
A database-native change-control system that detects and records changes to tables, indexes, views, triggers, procedures and functions, preserving an operational record alongside standard Git history.
- Database-side detection
- Schema & code history
- Release-change evidence
sqlDataTrans
Controlled data transfer, replication and synchronization.
An integration controller designed around high-throughput initial loads, change capture, synchronization validation and the managed transition into continuously updated SQL Server replicas for Data Warehouse environments.
- Parallel full loads
- Change synchronization
- Transfer supervision
04Why this matters to AI
Production AI begins with production-grade data.
Models cannot compensate for inaccessible, stale, inconsistent or untraceable data. We engineer the data boundary so the statistical component can be evaluated, operated and evolved against evidence.
05 / Engineer the foundation