I’m a Senior Data Engineer focused on building hybrid data platforms that combine large-scale batch analytics with low-latency realtime systems.
Over the last 9+ years, I’ve worked across AWS and Azure designing distributed architectures for analytics, machine learning, realtime decisioning, operational intelligence, and event-driven platforms.
My work typically sits at the intersection of:
- analytics engineering & ML infrastructure
- streaming systems & realtime processing
- distributed data platforms
- observability, governance, and reliability
- platform engineering & operational scalability
I enjoy designing systems that remain understandable, reliable, and operationally simple as they scale.
flowchart LR
%% Common Entry Points
src[🔌 Source Systems] ===> ingest[📥 Ingestion Layer]
%% Shared split out to processing paths
ingest ===> stream[Realtime Processing]
ingest ===> batch[Batch Processing]
%% Realtime Pipeline Lane
subgraph Realtime_Lane ["⚡ Realtime Processing Pipeline"]
stream ===> ops[⚙️ Operational Systems]
end
%% Batch Pipeline Lane
subgraph Batch_Lane ["📦 Batch Processing Pipeline"]
batch ===> ml[📈 Analytics & ML]
end
%% Common Convergence Destination
ops ===> platform[[💎 Data Platform & Serving Layer]]
ml ===> platform
%% --- MAXIMUM VISIBILITY LINK STYLING ---
linkStyle default stroke:#1e293b,stroke-width:3px;
%% --- HIGH CONTRAST LIGHT PROFILE ---
%% Common Framework Nodes (Lightened & Highly Visible)
style src fill:#f1f5f9,stroke:#475569,stroke-width:2px,color:#0f172a
style ingest fill:#dbeafe,stroke:#1e40af,stroke-width:2px,color:#1e3a8a
%% Realtime Path: Crisp Mint (Light background, Dark bold text)
style stream fill:#ccfbf1,stroke:#0d9488,stroke-width:2px,color:#115e59
style ops fill:#ccfbf1,stroke:#0d9488,stroke-width:2px,color:#115e59
%% Batch Path: Creamy Amber (Light background, Dark bold text)
style batch fill:#fef3c7,stroke:#d97706,stroke-width:2px,color:#78350f
style ml fill:#fef3c7,stroke:#d97706,stroke-width:2px,color:#78350f
%% Destination Nexus: Lavender Core with Deep Violet Text
style platform fill:#f3e8ff,stroke:#7c3aed,stroke-width:3px,color:#4c1d95
%% Structural Boxes: Soft Pastel Canvases
style Realtime_Lane fill:#f0fdfa,stroke:#5eead4,stroke-width:2px,color:#0f766e
style Batch_Lane fill:#fffbeb,stroke:#fde047,stroke-width:2px,color:#a16207
Recently, I’ve been exploring AI-assisted data engineering and intelligent data applications using orchestration frameworks, LLM workflows, and structured extraction pipelines.
Built containerised workflows using Dagster, PostgreSQL, Docker Compose, LiteLLM, and Gemini APIs for automated classification, enrichment, and analytical processing of large-scale unstructured datasets.
Current areas of exploration include:
- LLM-assisted data workflows
- intelligent extraction pipelines
- orchestration-driven AI systems
- AI-ready data platforms
- scalable analytical enrichment systems
while system.is_scaling():
prioritize(reliability)
reduce(complexity)
improve(observability)Nothing humbles a data platform faster than an “optional” field in production.