How I Work
My approach to designing, building, deploying, and maintaining reliable data platforms and scalable systems.
My Engineering Philosophy
I believe great data systems should be reliable, scalable, observable, secure, and easy to maintain.
Simplicity
Automation
Data Quality
Scalability
Security
Documentation
Why I Work This Way
Data engineering isn't just about moving data; it's about building trust. My structured approach is designed to eliminate ambiguity, reduce technical debt, and ensure that every pipeline I build delivers consistent business value.
By enforcing rigorous phases like discovery, architecture, and validation, I minimize costly production incidents and ensure the systems we build today are robust enough for the challenges of tomorrow.
My Project Lifecycle
Phase 1: Discovery & Requirements
Activities: Stakeholder meetings, requirement gathering, data source identification, success metrics, risk analysis.
Deliverables: Requirements document, solution proposal, architecture draft.
Phase 2: Architecture & Planning
Activities: Data flow design, infrastructure planning, technology selection, cost estimation, security review.
Deliverables: Architecture diagrams, technical design documents, sprint plan.
Phase 3: Development
Activities: Pipeline development, ETL implementation, infrastructure setup, testing, documentation.
Deliverables: Source code, automated tests, deployment scripts.
Phase 4: Deployment
Activities: CI/CD execution, monitoring setup, validation, performance testing.
Deliverables: Production deployment, monitoring dashboards, runbooks.
Phase 5: Optimization
Activities: Cost optimization, performance tuning, scaling, continuous improvements.
Deliverables: Optimization reports, updated architecture.
Applied Example: Real-Time MPESA Streaming Platform
Requirement: Process 10k transactions/sec with sub-second latency.
Architecture: Webhook -> Kafka -> Flink -> BigQuery.
Optimization: Partitioning tuning and Flink checkpointing optimization reduced latency by 40%.