Emeka Nweke Edeh

Data and platform engineer. Systems teams can trust.

I build production platforms across AWS and GCP, with real depth in cloud foundations, data movement, warehouse design, and analytics delivery.

Projects Writing

Berlin • AWS + GCP • Platform engineering

4+ Years delivering production platform work across infrastructure, transformation, and serving

1M+ Daily records processed in real-time delivery work with reproducibility and quality controls

AWS · GCP cloud environments used across architecture, platform delivery, and enterprise rebuild work

LLM Analytics analytics product work built on trusted Gold-layer data and guardrailed query flows

Selected outcomes

What the work actually adds up to.

Architecture depth matters most when it produces clean operating boundaries, reusable delivery patterns, and analytics systems people can use confidently.

Platform delivery

Multi-repo architecture with real boundaries

Infrastructure, CDC, transformation, orchestration, and analytics access kept separate without losing the end-to-end platform shape.

Cross-cloud standards

Enterprise patterns carried across AWS and GCP

The platform ideas are strong enough to hold their shape across identity, governance, storage, and environment design in a second cloud.

User-facing analytics

Trusted data turned into usable answers

Curated Gold data is surfaced through APIs, BI, and natural-language analytics instead of stopping at the warehouse layer.

What stands out

Platform work with stronger boundaries than a typical portfolio.

The focus here is not just tools used. It is architecture shape, delivery discipline, and how trusted analytics products get built on top.

Architecture

Systems-first platform design

Platform work shaped as a complete operating model, not just a pile of tools.

Delivery

Environment discipline that holds up

Reusable Terraform, controlled configuration, and clean boundaries across dev, staging, and prod.

Documentation

Technical depth explained clearly

Guides and notes written so the design decisions are visible, not only the final code.

Platform architecture

From source systems to trusted analytics.

A production flow spanning ingestion, transformation, warehousing, governance, and analytics delivery, presented as one operating system instead of disconnected parts.

Enterprise workflow

Production data moving from source systems into trusted analytics products and user-facing answers.

Terraform

multi-environment foundation

GitHub Actions

CI/CD and deployment control

Airflow

orchestration and scheduling

BigLake

governance and external access

Source PostgreSQL

transactional records

Change feed CDC + Kafka

capture and streaming

Bronze S3 + GCS

raw and replayable storage

Silver PySpark

validated current-state data

Gold dbt

business-ready marts

Warehouse BigQuery + Redshift + Athena

trusted analytics access

Serving Agent + BI

dashboards, APIs, NL analytics

AWS production platform GCP enterprise rebuild Dev, staging, prod discipline

Explore the work

Follow the platform from principles to implementation.

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