Senior Data & Cloud Engineer · PostgreSQL HA Specialist · AWS Certified
Building production-grade data infrastructure for healthcare and financial systems

Data Platform Engineer with 4+ years of experience specializing in PostgreSQL high-availability architecture (Patroni, ETCD, HAProxy), cloud data pipelines, and infrastructure for clinical, financial, and government datasets. Delivered measurable impact across employers i.e., 5x pipeline speedup, 33% cloud cost reduction, 60% faster queries through code refactoring, cloud data migrations, HA set-up, disaster recovery designs and close collaboration with client and cross-functional teams. Currently expanding into modern Postgres ecosystems (TimescaleDB, Supabase) as a PostgreSQL consultant at Stormatics, and backend API development (FastAPI microservices). EDB Postgres AI, AWS Certified Data Engineer and Kubernetes (KCNA) certified.

Languages

Databases & HA

Cloud & Infrastructure

Data Engineering

Backend & Messaging

Monitoring

Event-driven microservices ecommerce backend, built to practice production backend patterns alongside infrastructure work.

• Stack: FastAPI · PostgreSQL (database-per-service) · RabbitMQ · JWT · AWS SES · Docker
• Architecture: 8 services communicate via RabbitMQ topic exchanges — payment confirmed → shipment auto-created → customer notified to confirm delivery → auto-confirmed after 2hrs if no response. Each service owns its database; no cross-database queries
• Design: Shared-secret JWT verification across services, public-read/staff-write authorization, decoupled migrations, relational catalog modeling with size-based pricing

Production-grade PostgreSQL high-availability cluster built for clinical data infrastructure.

• Stack: Patroni · ETCD · HAProxy · pgBackRest · AWS NLB (af-south-1)
• Features: Automatic primary election, read/write splitting, S3-based WAL archiving, PITR disaster recovery
• Design: 3-node cluster across multiple AZs · NLB health checks on HAProxy ports 9000/9001 · Zero-downtime failover

High-throughput parallel ingestion pipeline.

• Cross-cloud schema synchronization with automated DDL generation
• PII encryption and data validation for clinical datasets
• CI/CD via GitHub Actions

Automated parallel report generation engine for high-volume stakeholder reporting, reducing processing time from 12 hours → 2.5 hours.

• M.Sc. Software Engineering — Quantic School of Business & Technology (August 2026)
• M.Eng. Chemical Engineering — Federal University of Technology, Minna (October 2021)
• B.Eng. Chemical Engineering — Federal University of Technology, Minna (November 2014)

Open to PostgreSQL consulting, data engineering contracts, and remote opportunities.
uchejudennodim@gmail.com ·