Christian – Python, Django, React

A promotion management platform designed to help customer retention specialists efficiently apply promotional offers to customer accounts. The platform integrates multiple internal and external APIs to apply key promotions across the customer base. The primary challenge involved orchestrating multiple independent micro front-end modules developed by different teams and seamlessly consuming data from various third-party providers to deliver a cohesive user experience.

• Led technical vision and project execution as provisional tech lead for the promotion management platform;
• Crafted comprehensive technical assessment and feasibility analysis for implementing a complex multi-team solution;
• Architected full-stack front-end and back-end solution using React, Next.js, Express, Webpack, and Typescript;
• Designed cohesive micro front-end architecture enabling multiple independent teams to develop and deploy modules without blocking each other;
• Built core front-end components and features for the promotion management interface;
• Implemented API integration layer to consume multiple third-party promotional data sources and services;
• Orchestrated integration of independently developed micro front-ends into a unified, seamless user experience;
• Collaborated closely with technical and non-technical stakeholders to align on requirements and technical approach;
• Established best practices for component development, testing, and deployment in a micro front-end environment;
• Managed technical debt and ensured code quality across distributed teams and codebases.

An agent-facing platform designed to help customer service representatives manage subscription, package, and product information through a unified interface. The platform enables call center agents to efficiently handle customer inquiries, make changes to accounts, and access real-time information while maintaining data security. Built on modern web technologies and cloud infrastructure with granular access controls to protect sensitive customer data.

• Led end-to-end development of an agent-facing platform serving thousands of customer service representatives;
• Architected full-stack solution using modern web technologies (Next.js, Express, GraphQL, Node.js) deployed on Google Cloud Platform;
• Designed and implemented microservices-based backend infrastructure with scalable APIs for managing subscription and product data;
• Implemented OAuth-based authentication and authorization layer providing granular role-based access control to protect sensitive customer information;
• Built responsive, intuitive front-end interface enabling agents to efficiently manage customer accounts and subscriptions;
• Developed comprehensive test suites and automated validation for existing and new components, APIs, and critical workflows;
• Integrated Contentful CMS for dynamic content management, allowing business teams to manage product information without code changes;
• Collaborated with business stakeholders and design teams to translate requirements into technical implementations;
• Optimized platform performance and implemented monitoring solutions to ensure reliability for high-volume agent usage;
• Established security best practices including data access controls, encryption, and compliance with industry standards.

Large-scale data migration project consolidating a legacy data entity to a modern, optimized core model. Ensured zero downtime for millions of users while improving platform performance, simplifying the data model, and enhancing observability. Required coordination across backend, operations, and business teams dependent on the legacy entity for critical advertising and marketing operations.

• Architected and developed the backend infrastructure to serve millions of users during the data migration with zero downtime;
• Led and coordinated full-stack development teams across front-end, back-end, and internal tools to execute the migration strategy;
• Designed and built the new core data model, optimizing for performance, scalability, and simplified schema architecture;
• Developed comprehensive data migration scripts and ETL pipelines to safely transfer historical data from the legacy system while validating data integrity;
• Implemented application logic to handle dual-write scenarios ensuring seamless transition between legacy and new systems;
• Managed and executed A/B testing framework to validate migration impact and measure performance improvements across user-facing features;
• Created automated test suites and validation scripts to ensure data consistency and integrity throughout the migration process;
• Collaborated with multiple backend, operations, and business teams to coordinate cutover planning and risk mitigation;
• Optimized query performance and API response times on the new data model to exceed legacy system benchmarks;
• Implemented comprehensive logging, monitoring, and observability solutions to track migration health and system performance.

An AI-powered data labeling and classification system that automates the process of generating accurate, structured labels and descriptions for large-scale unstructured data sets. The platform leverages language models to intelligently classify, tag, and annotate data with high precision, enabling organizations to build clean, validated datasets at scale. The system integrates with distributed data warehouses to process billions of data points and generates boilerplate classifications and labels that teams can customize and deploy across their operations. A key application involves intelligent filtering of irrelevant or low-quality data, improving data quality and operational efficiency.

• Architected and built an enterprise-grade data processing pipeline using Airflow, Python, and Apache Spark to handle billions of data points from distributed Hive warehouse tables;
• Developed data transformation and normalization logic to prepare raw data for AI-powered classification and labeling;
• Integrated language models with the data pipeline to enable automated, intelligent labeling and classification of unstructured data;
• Implemented system to send classified and labeled datasets to API endpoints for further processing and integration with downstream systems;
• Created a flexible, configurable system allowing stakeholders to define custom data sources and add multiple distributed data warehouses to the pipeline;
• Optimized data processing workflows to maximize classification accuracy while minimizing processing latency;
• Debugged complex data pipeline issues and resolved system failures impacting data delivery and classification quality;
• Collaborated with business stakeholders, data providers, and API partners to define data requirements and delivery specifications;
• Implemented comprehensive unit tests and integration tests to ensure system reliability and data quality validation across the pipeline.

A visual discovery and content curation platform that enables users to discover, save, and organize content through rich, visual feeds. The platform provides content creators and marketers with a hub to curate and share themed collections across diverse categories including DIY projects, lifestyle, fashion, and business inspiration. Built with scalable architecture to support millions of daily active users discovering and engaging with visual content across multiple surfaces and campaigns.

• Architected and optimized dimensional data models to support high-volume analytics workloads across the discovery platform;
• Developed advanced SQL queries and PySpark jobs to process complex data transformations on user interaction data, engagement metrics, and campaign performance signals;
• Built end-to-end ETL pipelines leveraging data warehouse infrastructure to consolidate multi-source data from platform surfaces, user behavior logs, and marketing campaigns;
• Designed and implemented interactive dashboards and visualizations to expose key business metrics across campaign performance, user engagement, and content curation effectiveness;
• Collaborated with product and marketing stakeholders to understand reporting requirements for content curators and campaign managers, translating requirements into scalable analytics solutions;
• Optimized reporting workflows to reduce data processing latency and enable real-time insights into content performance across platform surfaces;
• Created automated analytics reports delivering clear performance metrics to content curation teams, reducing manual report generation time by 40%.