Backend systems and data
platforms built for
production, not demos.

I'm Julius, software and data engineer based in Lagos, Nigeria. I have 6 years+ experience shipping APIs, pipelines, and cloud infrastructure at places like Moniepoint and Turing, where things breaking at 3am is not an option.

Let's Talk Explore Case Studies
5M+ Daily Events Processed
99.95% Uptime at Moniepoint
50+ Systems in Production
47ms Avg API Response Time
What I Deliver

The work,
broken down.

Six areas I've shipped in production, from APIs and pipelines to cloud infra and ML backends.

Backend Software Engineering

Design and build production-grade APIs, microservices, and distributed systems in Python and Node.js. From REST and GraphQL services to event-driven architectures, engineered for maintainability from day one.

99.9% API Uptime · 40ms p99

Data Pipeline Engineering

High-throughput, fault-tolerant ETL/ELT pipelines using Airflow, dbt, and Spark. From batch ingestion to sub-second streaming, built to not fail.

↓35% Pipeline Failure Rate

Cloud Architecture

Tri-cloud certified across AWS, GCP, and Azure. Design cloud-native infrastructure that's cost-efficient, observable, and production-hardened.

20% Cost Reduction

Platform Reliability & Observability

Centralised logging, alerting, and SLA monitoring across both software and data systems. Helped lift platform SLA from 96% to 99.9% and cut MTTR by 35% in a live fintech production environment.

SLA 96% → 99.9%

Real-Time Streaming

Kafka-powered event streaming pipelines delivering transaction and operational data with under 5-second end-to-end latency.

<5s Streaming Latency

DevOps & Containerisation

End-to-end IaC with Terraform, Docker, and Kubernetes. CI/CD pipelines that deploy both software and data systems reliably and repeatably.

3x Faster Deploy Cycle

Scalable System Design & Integration

Architect and integrate complex software systems: monolith-to-microservice migrations, third-party API integrations, database schema design, and service mesh patterns. Built to grow without rewrites.

50+ Production Systems Shipped

AI-Enabled Solutions

Productionise ML models at scale: feature pipelines, inference infrastructure, and intelligent features embedded directly in software products.

+15% Fraud Detection
Measurable Results

What the work
produced.

5M+
Daily Events Processed

Scalable event streaming pipelines at Moniepoint handle millions of transactions daily across distributed fintech infrastructure, with 40% lower latency than the prior architecture.

Moniepoint Group · 2025–Present
99.95%
Platform Uptime

Centralised observability, real-time alerting, and proactive incident management drove platform reliability from 96% to 99.95% SLA. Regulated fintech, no room for downtime.

Moniepoint Group · 2025–Present
60%
Faster Fine-Tuning Cycles

Accelerated AI model iteration cycles by offloading heavy data preprocessing and tokenization to distributed Celery background workers.

Turing.com · 2024–2025
35%
Faster Incident Response

MTTR cut by 35% through centralised logging, structured alerting, and a unified observability stack that made root-cause analysis immediate rather than investigative.

Moniepoint Group · 2025–Present
20%
Compute Cost Reduction

Lowered AI infrastructure overhead with a Django-managed caching layer that prevented redundant feature extraction and model retraining loops.

Turing.com · 2024–2025
100+
ETL Pipelines in Production

Designed, built, and maintained over 100 production ETL pipelines with 99% data accuracy. Reduced manual data processing time by 35% and increased dataset reusability across business units.

UCARD Innovations · 2022–2024
Service Areas

What I
work on.

Backend Software Engineering

Design and build production-grade APIs, microservices, and distributed systems. Whether it's a greenfield product, a legacy modernisation, or a performance-critical integration, engineered with clean architecture and built to last.

Problems I solve

Monolithic systems that can't scale or be deployed independently
Slow, brittle integrations breaking under production load
No test coverage, making changes risky and slow
Inconsistent APIs that block frontend and partner teams

Outcomes delivered

50+ production software systems shipped end-to-end
99.9% API uptime with sub-40ms p99 latency
Monolith-to-microservice migrations with zero downtime
3x faster release cycles through CI/CD and test automation

Industries

Fintech SaaS Enterprise EdTech

Languages

PythonNode.jsTypeScriptC++

APIs & Frameworks

FastAPIDjangoExpress.jsRESTGraphQL

Databases

PostgreSQLMySQLRedisMongoDB

Architecture Patterns

MicroservicesEvent-DrivenCQRSDDD

Testing & Quality

pytestJestTDDCI/CD

Scalable Data Platforms

Design and build modern data platforms that ingest, transform, and serve high-volume data reliably. Whether batch or streaming, cloud or hybrid, built with the tools your team will actually maintain.

Problems I solve

Pipelines that silently fail and corrupt downstream data
ETL jobs that take hours and can't scale horizontally
Data silos blocking cross-functional analytics
Missing lineage, auditability, and data governance

Outcomes delivered

100+ production pipelines with 99% data accuracy
2-hour batch jobs reduced to 45 minutes
35% reduction in pipeline failure rates
Real-time streaming with <5s end-to-end latency

Industries

Fintech Telecoms Retail Education

Orchestration

Apache AirflowdbtLuigi

Processing

Apache SparkPySparkPythonSQL

Streaming

Apache KafkaAWS KinesisDataflow

Warehousing

RedshiftBigQuerySnowflakePostgreSQL

Modelling

KimballStar SchemaData VaultOLAP

Cloud-Native Infrastructure

Tri-cloud certified (AWS, GCP, Azure). Design, migrate, and optimise cloud data infrastructure that reduces cost, improves reliability, and scales without engineering heroics.

Problems I solve

Overprovisioned, expensive on-premise data infrastructure
Cloud environments without observability or cost controls
Multi-cloud data movement complexity
Warehouse migrations causing downtime and data loss

Outcomes delivered

20% infrastructure cost reduction post-migration
60% query performance improvement on Snowflake
Zero-downtime warehouse migration execution
Centralized observability across 15+ services

Certifications

AWS Certified Data Analytics GCP Pro Data Engineer Azure DP-203

AWS

S3GlueEMRRedshiftLambda

Google Cloud

BigQueryDataflowDataprocGCS

Azure

Data FactorySynapseADLS

DevOps & Infrastructure as Code

Unreliable infrastructure makes everything else harder. I build reproducible, version-controlled environments with CI/CD pipelines so deployments are boring, not stressful.

Problems I solve

Manual, undocumented infrastructure that can't be reproduced
Slow deployments that block data team velocity
Missing alerting and incident detection
Security gaps in cloud-native fintech infrastructure

Outcomes delivered

30% reduction in critical production incidents
45% faster incident detection time
Full infrastructure reproducibility with Terraform
RBAC implementation eliminating unauthorised access

Containerisation

DockerKubernetes

Infrastructure as Code

Terraform

CI/CD & Version Control

GitHub ActionsGitCI/CD Pipelines

Observability

Centralised LoggingAlertingRBAC

Analytics Engineering & BI

Connect data platforms to business decisions. Build analytics layers and dashboards that translate operational data into actionable intelligence, fast enough to act on.

Problems I solve

Slow dashboards that executives don't trust
Excel-based reporting that doesn't scale
No single source of truth across business units
Decision-making delayed by data unavailability

Outcomes delivered

Dashboard load time reduced from 25s to 5s
Executive decision turnaround reduced by 50%
Daily revenue forecasting model with 18% RMSE reduction
Product adoption boosted 12% through analytics-driven features

BI Tools

Power BIdbt

Data Modelling

KimballStar SchemaOLAP

Languages

SQLPythonDAX

Databases

PostgreSQLMySQLBigQuery

AI & ML Operations

Bridge the gap between data science and production. Build the feature pipelines, infrastructure, and deployment workflows that take models from notebooks to live business systems.

Problems I solve

ML models stuck in notebooks that never reach production
Feature pipelines that don't match training/serving data
No monitoring for model drift or data quality
Fraud and anomaly detection with unacceptable false-positive rates

Outcomes delivered

Productionised ML models increasing fraud detection by 15%
Revenue forecasting model with 18% RMSE improvement
Churn reduction strategies from analytics-driven pattern detection
MSc in Artificial Intelligence (Covenant University, 2024)

Languages & Frameworks

PythonPySparkSQL

Pipeline Infrastructure

AirflowSparkKafka

Academic Foundation

MSc Artificial IntelligenceMSc Computer Science
How I Work

How an engagement
actually runs.

I don't start writing code until I understand the problem. Here's what working together looks like.

01

Discovery

Map data sources, business requirements, SLA expectations, and current pain points. No architecture before the problem is fully understood.

02

Architecture

Design the data model, pipeline topology, cloud infrastructure, and technology selection aligned to scale, cost, and reliability goals.

03

Implementation

Build pipelines, warehouse schemas, and infrastructure with test coverage, documentation, and CI/CD integration from day one.

04

Optimisation

Profile query execution, identify bottlenecks, tune indexing, and right-size compute. Performance is not an afterthought.

05

Observability

Deploy centralised logging, alerting, and data quality monitoring so issues surface in minutes, not hours.

06

Continuous Improvement

Handover with full documentation. Available for ongoing optimisation, incident support, and iterative capability expansion.

Selected Work

Problems solved,
results shipped.

Software systems, data platforms, and cloud migrations. Different challenges, one approach: production-ready and measurable.

Moniepoint Group 2025 – Present

Fintech Platform Engineering at 5M Events/Day

Challenge

To build a highly scalable distributed data infrastructure capable of processing millions of daily transactions to meet strict regulatory and operational demands, while successfully overcoming architectural bottlenecks that previously risked a 96% SLA.

Approach

Redesigned distributed data pipelines to ingest and process 5M+ daily events. Implemented centralised data observability across the entire data infrastructure. Tightened security posture and compliance tooling for data governance.

PythonKafkaAirflowAWSTerraformDocker
99.99%SLA Uptime (from 96%)
5,000+/sPeak Ingestion Throughput
85%Faster Data Delivery
100%Compliance Audit Pass Rate
40%Infra Cost Reduction
Turing.com 2024 – 2025

AI Service Backend Engineer

Challenge

Build a high-performance backend capable of handling high-throughput text preprocessing, tokenization, and model inference loops for domain-specific AI models. A structured, low-latency API layer was needed to clean raw user prompts, inject specialised datasets, and manage fine-tuned model weights dynamically, without slow response times or contextual inaccuracies.

Approach

Engineered a high-performance backend using Python and Django to orchestrate the entire AI fine-tuning and inference lifecycle. Built asynchronous background tasks for heavy text-chunking, tokenization, and data curation. Delivered optimised REST APIs to safely manage model checkpoints, serve fine-tuned model outputs, and handle distributed vector-embedding syncs.

Python & DjangoDjango REST FrameworkCelery & RedisHugging FacePinecone / pgvector
60%Faster Fine-Tuning Cycles
20%Compute Cost Reduction
<5sInference API Latency
+15%Task-Specific Accuracy Gain
UCARD Innovations 2022 – 2024

Software Platform & Analytics Transformation

Challenge

A legacy software stack with tightly coupled services, manual deployment processes, and an analytics layer built on fragmented Excel workflows. Dashboards took 25 seconds to load and deployments took days.

Approach

Refactored core services into independently deployable modules with CI/CD pipelines. Migrated analytics to a centralised PostgreSQL store, built 100+ ETL pipelines, and delivered Power BI dashboards with a revenue forecasting model achieving an 18% RMSE improvement.

PythonPostgreSQLAirflowPower BIDockerGitHub Actions
3xFaster Deploy Cycle
5sDashboard Load (from 25s)
50%Faster Decisions
99%Data Accuracy
Technology Stack

Production-proven across
software & data.

Tri-cloud certified. Every tool listed has been used in production systems, not just side projects.

Languages

Python TypeScript / Node.js SQL PySpark C++

Software Engineering

FastAPI Django Express.js REST & GraphQL Microservices

Data Engineering

Apache Airflow dbt Luigi Apache Spark Apache Kafka

Cloud - AWS

Amazon S3 AWS Glue Amazon EMR Amazon Redshift

Cloud - GCP

BigQuery Cloud Storage Dataflow Dataproc

Cloud - Azure

Azure Data Factory Synapse Analytics Data Lake Storage

Databases

PostgreSQL MySQL Snowflake BigQuery Redshift

DevOps & Infra

Docker Kubernetes Terraform CI/CD Git

Analytics & BI

Power BI dbt (Analytics Eng.) Kimball / Star Schema Data Vault
AWS
AWS Certified Data Analytics
Amazon Web Services
GCP
Professional Data Engineer
Google Cloud
AZ
Azure Data Engineer Associate
Microsoft · DP-203
What People Say

What people
say.

Julius rebuilt our data infrastructure from the ground up. What was a chaotic mess of failing batch jobs and unobservable services became a reliable, scalable platform we actually trust. The SLA improvement from 96% to 99.9% speaks for itself.

Ojuola Oluwamayowa
ucard.store

The Snowflake migration could have been a disaster. Julius planned it meticulously, zero downtime, 60% faster queries on day one. Our data science team now has real-time access to transaction data they've been asking for for two years.

Adebanjo Olajide
kudiwallet.ng

Not just an engineer, a business thinker. Julius consistently linked every technical decision to a measurable business outcome. Our executive dashboards went from 25-second load times to near-instant, and decision cycles compressed by half.

Marcin Plichta
Turing.com
About Julius

Six years building
things that run.

I'm Julius Odunuga, a Software and Data Engineer based in Lagos, Nigeria with over 6 years of production experience. I've worked across the full stack: backend APIs, microservices, data pipelines, cloud architecture, real-time streaming, and analytics.

I've shipped in regulated fintech environments where correctness and uptime aren't optional. At Moniepoint, I engineered pipelines processing 5M+ daily events at 99.99% SLA. At Turing, I built the backend behind a fine-tuned AI service and cut iteration cycles by 60%. At UCARD, I refactored core services and built the analytics layer that halved decision turnaround time.

"My engineering philosophy centers on building highly autonomous systems that minimize manual operational overhead. I focus heavily on root-cause analysis and architectural stability, eliminating recurring technical debt and ensuring system reliability at scale."
Full-stack engineering background: backend APIs, microservices, data pipelines, and cloud infrastructure
Tri-cloud certified: AWS, Google Cloud, and Microsoft Azure data engineering certifications
MSc Artificial Intelligence (Covenant University, 2024), with a second MSc in Computer Science
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Julius Odunuga
6+ Years shipping software & data systems
3 Cloud certifications (AWS · GCP · Azure)
50+ Production software systems shipped
2 Master's degrees (AI + Computer Science)

Education

MSc Artificial Intelligence · 2024 Covenant University
MSc Computer Science · 2023 Olabisi Onabanjo University
BSc Computer Science · 2018 Olabisi Onabanjo University
Let's Work Together

Got something
you need
built or fixed?

Drop me a message or book a call. I'll tell you honestly whether I can help, and what that would look like.

odunugajulius1@gmail.com
linkedin.com/in/julius-odunuga
Lagos, Nigeria · Available Globally (Remote)

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Typically responds within 24 hours. All conversations are confidential.