Darren M.
Backend Developer
Notable Highlight
Led the technical transition for Ren. from a manual, sheet-based operational model to a fully centralized backend system. Built proprietary APIs managing the entire subscription lifecycle, data ingestion, and verification processes, eliminating reliance on manual data entry.
Experience Summary
Darren is a technically strong Backend Developer and Data Specialist with 4 years of experience building scalable backend systems, automation workflows, and operational analytics infrastructure within high-growth e-commerce environments. With an academic background in Actuarial Science from the University of Johannesburg, he combines analytical problem-solving with hands-on backend engineering expertise across Python, SQL, BigQuery, and cloud infrastructure. Darren gained extensive backend Shopify experience while working at Ren., a subscription-based e-commerce business, where he played a key role in supporting and optimizing the company's Shopify ecosystem, backend operations, payment infrastructure, and customer subscription workflows. His work involved building integrations, automating operational processes, managing data flows between platforms, and enhancing the systems that supported the end-to-end subscription customer lifecycle. He became a key contributor to the company’s internal infrastructure, architecting FastAPI microservices, developing ETL pipelines, automating payment and collections workflows, and building real-time operational platforms used across Payments, Collections, and Business Intelligence teams. He has strong experience integrating APIs, optimizing business processes, and leveraging cloud-native tools to improve scalability, reporting accuracy, and operational efficiency.
Key Achievements
- Darren was a core backend developer involved in launching Ren. Drive, a strategic partnership between Ren. and Avis. He helped build the backend infrastructure supporting vehicle subscription management and “Rent-to-Own” workflows, enabling scalable management of high-value automotive assets through automated collections and operational systems. - He architected and maintained FastAPI-based microservices integrated with BigQuery, MySQL, Pub/Sub, and ETL pipelines, automating previously manual operational workflows and significantly improving scalability, data accessibility, and operational efficiency across multiple departments. - Darren developed automation and analytics solutions that reduced payment processing errors by 50%, automated over 90% of manual payment processes, improved collections efficiency by 25%, and reduced operational costs by nearly 50% through real-time reporting, workflow automation, and advanced data processing systems.
Skills
Backend Development, FastAPI Microservices Architecture, API Development & Integrations, Secure API Design (authentication, rate limiting, schema validation), Python Development (Pandas, NumPy), Flask & Django Frameworks, Data Engineering & ETL Pipelines, BigQuery & Google Cloud Platform (GCP), MySQL Database Management, Redis Caching, Docker-based Deployment, Pub/Sub Integrations, Web Scraping & Automation, Workflow Automation & Process Optimization, Feature Engineering & Customer Segmentation, Operational Analytics & KPI Automation, Business Intelligence Reporting, Real-Time Data Pipelines, Third-Party API Integrations (payment gateways, MDM systems), SQL Query Optimization, Payment Systems & Collections Analytics, Risk Assessment & Delinquency Detection, Billing Validation Automation, Data Normalization & Information Management, Looker Studio Dashboarding, Cross-Functional Stakeholder Collaboration, Technical Requirements Translation, Business Process Optimization, Analytical Problem-Solving, Tools & Platforms, Python, FastAPI, Flask, Django, MySQL, BigQuery, Google Cloud Platform (GCP), Redis, Docker, ETL Pipelines, Hevo, Git, Looker Studio, Jira, Excel, Canva, R Programming Language
Work Experience
Backend Developer
Jan 2024 – Present
- Architect and maintain FastAPI-based microservices powering internal operational platforms.
- Enforce secure API design with authentication, rate limiting, and strict schema validation using Pydantic.
- Integrate APIs with BigQuery, MySQL, Pub Sub, and ETL workflows, enabling real-time and batch data processing.
- Improve API performance and scalability through Redis caching and Docker-based deployment.
- Automate previously manual workflows, increasing operational efficiency and reducing human error.
- Build ETL pipelines (Python, BigQuery) for feature engineering to extract and structure records for the entire business customer base.
- Develop SQL queries reducing payment processing errors by 50% while enabling quantitative analysis of customer patterns.
- Identify and analyze payment system inefficiencies and automate 90% of processes, reducing operational costs by near 50% while improving compliance with internal policies.
- Play a key role in developing an internal centralized platform onboarded and used by Collections, Payments, and Business Intelligence teams to perform daily tasks, access real-time metrics, and continuously optimize business processes.
- Automate KPI reporting (recovery rates, failed debits) for collections teams, improving collections efficiency by 25%.
- Collaborate with stakeholders to develop real-time data pipelines that improve decision-making by 40% through enhanced BI reporting.
- Translate technical requirements into API solutions that integrate with 3rd Party Platforms, cross departmental platforms, and customized data solutions, reducing manual interventions by 80% and enhancing information management, feature engineering, and asset visibility.
- Implement Python scripts to validate charge cycles, preventing monthly billing errors on a 15,000+ customer base.
- Test and implement automated payment process validation using MySQL, decreasing processing time by 85% while maintaining near 100% accuracy in internal risk assessment metrics.
Payment Systems Analyst (Graduate Program)
Ren. · March 2023 – Dec 2023
- Analyze customer profile histories to segment delinquent customers, increasing collections by 15%.
- Replace manual Excel reports with customized excel formulae to extract business level insights.
- Develop early phase automation models.