Jonas Dimaculangan

Engineered and maintained scalable, high-throughput job ingestion pipelines using Python, Selenium, HTTP, and API collectors, optimizing with multithreading and retry logic to reliably process large volumes of diverse job data.

Developed real-time analytics and data quality dashboards in Metabase, enhancing visibility into job coverage, throughput, and deduplication rates to enable faster issue detection and data-driven decisions.

Implemented CI/CD automation using GitHub Actions for Dockerized scraping and processing services, enabling centralized container log streaming and runtime visibility via Dozzle for robust production operations.

Integrated Sentry for structured logging, error tracking, and alerting, significantly improving observability across distributed pipelines and reducing diagnostic and resolution times for production issues.

Built robust data processing and enrichment workflows in Python on GCP with PostgreSQL, leveraging OpenAI for AI-assisted extraction and geocoding to ensure consistent, high-quality structured data.

Designed and deployed end-to-end automation ecosystems with n8n and Make.com, orchestrating multi-platform workflows across key business applications to streamline operations and eliminate manual work.

Integrated AI voice agents (Vapi, Bland AI) into n8n/Make workflows, creating autonomous communication pipelines for lead qualification, customer follow-ups, and appointment scheduling.

Developed custom Python and JavaScript scripts to extend workflow capabilities, including data parsing, API integrations, and process optimization, exceeding native automation limits.

Automated data synchronization and reporting across CRMs and internal systems with real-time alerts, improving team responsiveness and operational visibility.

Implemented robust scheduling, logging, and tracking systems using Google Sheets and Airtable, ensuring reliable execution, transparent audit trails, and efficient performance monitoring.

Delivered modular, reusable automation components, reducing deployment time and manual operations by over 70% and enabling rapid scaling of AI-driven business processes.

Designed and maintained a large-scale Job Application Tracking System (ATS) managing over 35M job listings and automating data collection across 40+ job platforms.

Built high-performance Python crawlers and automation bots using Playwright, Selenium, and Requests, implementing multi-threading, async execution, and proxy rotation for stable, high-throughput scraping.

Developed web automation scripts for autofilling job applications, handling CAPTCHA, Cloudflare, and Akamai challenges through adaptive browser behavior and smart retries.

Implemented robust data pipelines and scheduling systems for continuous scraping, data refresh, and validation, ensuring consistent uptime and reliable updates.

Optimized MongoDB and PostgreSQL workflows for performance and scalability, improving query efficiency and reducing data duplication and latency.

Integrated backend modules to generate enriched, standardized, and AI-optimized job descriptions, significantly improving data quality and automation coverage.

Enhanced system performance and scalability via multithreaded architecture, asynchronous task execution, and database optimization, reducing processing time and resource usage across large scraping workloads.

Developed and maintained automated web scraping pipelines using Python, Scrapy, and Playwright to collect structured health, wellness, and product data from public and e-commerce sources.

Implemented proxy rotation, CAPTCHA-solving, and anti-bot evasion strategies to maintain high data accuracy and scraping continuity across large-scale operations.

Designed efficient data cleaning, transformation, and validation workflows, ensuring data consistency and usability for research and analytics teams.

Stored and synchronized processed data in Google Sheets and MongoDB, enabling real-time access for health researchers and data analysts.

Developed and maintained Python-based automation scripts for data extraction, transformation, and validation, streamlining research and reporting workflows across internal projects.

Built and deployed Flask REST APIs for lightweight data services and internal dashboards, gaining experience in backend development and API design best practices.

Applied Pandas, NumPy, and scikit-learn for exploratory data analysis and developed small-scale machine learning prototypes for predictive insights and data-driven reporting.

Collaborated with cross-functional teams using Git, GitHub, and Agile, contributing to version-controlled, code-reviewed development cycles.

Integrated third-party APIs and external data sources to enrich analytics pipelines, focusing on reliability, error handling, and performance optimization.

Assisted in cloud-based deployments and automation of development environments, gaining early experience with containerization, virtual environments, and API hosting.