Data Engineer specialized in designing and maintaining fault-tolerant, high-throughput distributed data architectures.
Focusing on the operational data lifecycle: scalable batch/stream ingestion, deterministic transformations, structured data lakehouse layers, and high-fidelity analytical consumption.
Data Engineering • Distributed Systems • Lakehouse Architecture • Apache Spark • Data Observability • Cloud Infrastructure
- Enterprise Cloud Platforms — Production lakehouses via Microsoft Fabric and Azure Data Services
- Distributed Computing Engines — Large-scale transformations leveraging Apache Spark and PySpark
- Lakehouse Topology — Medallion architecture implementation (Bronze ingestion, Silver cleansing, Gold analytics)
- Data Reliability & Observability — Automated schema evolution, proactive circuit breakers, and lineage validation
- Workflow Orchestration — Idempotent pipeline design and automated dependency graphs
[ Ingestion Vector ]
APIs • Streaming Events • RDBMS
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[ Processing Engine ]
Batch Processing • Micro-Batch
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┌───────────────────────┐
│ Storage Subsystem │
├───────────────────────┤
│ Bronze : Raw State │
│ Silver : Cleansed │
│ Gold : Dimensional │
└───────────────────────┘
│
▼
[ Serving Subsystem ]
OLAP • Machine Learning • APIs
| Domain | Technical Scope |
|---|---|
| Pipeline Engineering | Resilient batch/streaming extraction and load routines |
| Microsoft Fabric | Native Delta Lake workspaces, automated dataflows, and notebook execution |
| Distributed Processing | PySpark memory management, partition skew reduction, and shuffle tuning |
| Data Quality Frameworks | Declarative validations, schema monitoring, and unit-tested pipelines |
| Analytical Modeling | High-performance dimensional models (Star & Snowflake schemas) |
Low-latency machine learning compute engine built with C++ and exposed via native Python bindings.
MorphosML demonstrates core algorithm architecture and vector mathematics implemented without heavy external framework abstractions. Key components include optimized linear algebra kernels, K-Nearest Neighbors implementation, vectorized evaluation metrics, and full zero-copy NumPy memory layout interoperability.
C++ • Python • pybind11 • NumPy C-API • Algorithmic Optimization
Distributed Storage Protocols • Query Optimizer Execution Plans • Delta Lake ACID Internals • Advanced Dimensional Modeling • Site Reliability for Data Workflows
"Your heart is free. Have the courage to follow it."
— Braveheart
