Ogbonna Basil basilcea

Hi, I'm Basil Ogbonna 👋

Senior Data Engineer • Data Platform Engineer • AI & Agentic Systems Builder

I’m a Senior Data Engineer with 6+ years of experience designing and building reliable data platforms, batch and real-time pipelines, cloud infrastructure, and analytics systems.

My background spans Data Engineering, Data Platforms, Cloud Infrastructure, and Software Engineering. I'm increasingly focused on AI Engineering and Agentic Systems — particularly how LLMs, agents, data infrastructure, and human-in-the-loop workflows can be combined to build useful production systems.

I enjoy solving complex engineering problems and turning them into reliable, scalable, observable, and maintainable systems.

👨‍💻 About Me

🔧 Building scalable batch, streaming, CDC, and API data pipelines
🏗️ Designing data platforms, lakehouses, warehouses, and cloud infrastructure
🤖 Building and experimenting with AI agents, RAG, MCP, and multi-agent systems
☁️ Working across AWS, Azure, Kubernetes, Snowflake, and Databricks
📊 Designing systems around data quality, observability, lineage, and governance
⚙️ Automating infrastructure and delivery with Terraform, CI/CD, Docker, and Kubernetes
🧠 Exploring AI-native developer tooling and autonomous engineering workflows
💼 Open to Senior / Lead / Staff Data Engineering, Data Platform, and AI Engineering opportunities

🛠️ Tech Stack

Languages

Data Engineering

Data Platforms & Storage

Cloud & Infrastructure

Data Quality, Governance & Observability

AI & Agentic Systems

🏗️ What I Build

I work across the full data lifecycle:

Sources → Ingestion → Processing → Storage → Transformation → Quality → Analytics → AI

Typical architectures I work with include:

APIs / Databases / Events / CDC
              │
              ▼
     ┌─────────────────┐
     │    Ingestion    │
     │                 │
     │ Airbyte         │
     │ Kafka           │
     │ Debezium        │
     │ Python          │
     └────────┬────────┘
              │
              ▼
     ┌─────────────────┐
     │   Processing    │
     │                 │
     │ Spark           │
     │ Flink           │
     │ dbt             │
     └────────┬────────┘
              │
              ▼
     ┌─────────────────┐
     │ Data Platforms  │
     │                 │
     │ Snowflake       │
     │ Databricks      │
     │ Lakehouse / DWH │
     └────────┬────────┘
              │
      ┌───────┼─────────┐
      │       │         │
      ▼       ▼         ▼
  Analytics   ML/AI   Agentic
    & BI              Systems

📊 Engineering Impact

Some of the outcomes I've delivered while working on production data systems:

Built and maintained 30+ production data pipelines
Processed millions of records per day
Improved data accuracy by approximately 35%
Reduced source onboarding time by approximately 60%
Reduced data incidents by approximately 30%
Built streaming systems with up to 99.99% availability
Improved reporting performance by approximately 25%
Reduced data infrastructure and processing costs by approximately 30%

⚡ Data Engineering

My experience covers both batch and real-time data systems.

Batch

Python • Airflow • dbt • Spark • Snowflake • Databricks

Streaming & CDC

Kafka • Debezium • Spark Structured Streaming • Flink • Snowpipe Streaming

Data Quality

Soda • dbt Tests • SQL Validation • Reconciliation

Governance

DataHub • RBAC • Data Lineage • Masking • Audit Logs

Infrastructure

Kubernetes • Docker • Terraform • GitHub Actions • Azure DevOps

🤖 AI Engineering

I'm particularly interested in building AI systems where models are part of a larger engineered system, rather than standalone chat interfaces.

That includes:

Multi-agent architectures
Tool-using AI agents
Model Context Protocol
RAG pipelines
Vector search
Agent orchestration
Model routing
Human-in-the-loop workflows
AI-assisted software engineering
AI-assisted data engineering
LLM observability
Deterministic guardrails around probabilistic systems

🧠 Current Interests

I'm currently exploring the intersection of:

Data Engineering + Data Platforms + AI Engineering + Agentic Workflows + Developer Infrastructure

Particularly:

Agentic software engineering
AI-native data platforms
Model Context Protocol
Multi-agent orchestration
RAG and knowledge systems
AI-assisted data engineering
Human-in-the-loop autonomous systems
Open-source data infrastructure
Production LLM architecture
AI developer tooling

🧪 Engineering Philosophy

A few principles I try to follow when building systems:

Reliability before complexity
Automate repetitive work
Observability is part of the product
Data quality should be designed, not patched later
Infrastructure should be reproducible
AI should augment engineering judgment, not replace it
Human approval belongs at high-impact decision points
Build reusable platforms instead of solving the same problem repeatedly

🌱 What I'm Currently Building

I'm currently spending most of my personal engineering time exploring:

StackForge — AI-native data platform engineering

Matrades — Multi-agent market research and decision-support systems

Stylmi / Styler — Generative AI and virtual fashion experiences

Alongside experiments involving:

MCP • LLMs • RAG • Agents • Kubernetes • Open Source Data Infrastructure

🤝 Let's Connect

I'm interested in conversations and collaborations around:

Data Engineering • Data Platforms • Distributed Systems • Cloud Infrastructure • AI Engineering • Agentic Systems • Open Source

🌐 Portfolio: basilcea.vercel.app

💼 LinkedIn: https://linkedin.com/in/basilcea

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