Currently working on Cache Ads Infra, a high-scale and customized caching system built on top of an object storage. Processing billions of daily requests, the system ensures low-latency data access for global ad delivery, aiming to maximize revenue growth while streamlining operational expenditures.

Highlights

• Cost Efficiency: Optimized C++ core components, yielding $250K annual savings via CPU/Memory efficiency.

• Technical Debt: Refactored legacy codebases, reducing development overhead by $400K and accelerating time-to-market.

• Infrastructure: Migration to unified Cache Ads Infra, achieving $750K in annual savings through consolidation.

• Leadership: Scaled elite engineering teams by conducting system design, behavioural, and coding interviews. Mentored and onboarded senior and junior engineers to ensure rapid integration and technical growth.

• Product Roadmap & Vision: Defined and executed a multi-half technical roadmap for Cache Ads Infra, prioritizing high-impact optimizations that aligned infrastructure capabilities with Meta’s global ad-delivery goals.

• Cross-Functional Leadership: Served as the primary technical bridge for partner engineering teams, de-risking complex system integrations and ensuring seamless adoption of unified caching technologies across diverse product groups.

• Performance Research and A/B tests: Conducted data-driven studies to optimize system parameters, significantly improving revenue and reducing costs.

• Architecture & Design: Authored and conducted rigorous peer reviews of technical design documents to ensure scalable, high-performance implementations.

Most Used Technologies: C++, LLM-Assisted Development, AI-Driven Code Optimization, Apache Thrift, Object Storage, Containers, A/B tests

Tech Lead with expertise in developing and enhancing inventory management solutions, serving millions of merchants and processing millions of daily requests. I led 10+ projects from start to production, focusing on cost-effectiveness, scalability, performance, and observability.

Skilled in stakeholder management, roadmap planning, and delivery. Experienced mentor to senior and junior engineers, providing one-on-one coaching and live coding reviews to drive skill growth and timely solution delivery. Involved in interviewing and hiring senior and junior software engineers, identifying top talent to join high-performing teams.

Notable achievements include:

• Achieved significant performance boost in displaying live inventory levels, reducing latency from 30 minutes to 10 seconds, as featured in Shopify's 2023 Engineering Year in Review

• Delivered cost-effective solutions, reducing request time from seconds to milliseconds, saving thousands of dollars and enhancing experience for merchants and buyers

• Develop junior engineers into senior engineers.

Responsibilities:

• Product roadmap definition with stakeholders

• Lead engineers to deliver good products

• Write and review tech design docs

• Mentor senior and junior software engineers

• Conduct interviews and participate in hiring committees

• Optimize database queries and indexes

• Improve Observability and simplify the system

• Resolve concurrency problems

• Develop new features

• API design

Most Used Technologies: Ruby, Rails, Lua, Redis, MySQL, ElasticSearch, Background Jobs

As a Tech Lead, my core responsibilities were to improve communication with stakeholders and the quality of the systems, define the roadmap, reduce the time needed to deliver features and mentor engineers. To achieve that, I led several projects that significantly contributed to the company's success. I also trained over 20 seniors and software engineers in the Python ecosystem, including Python language and Django, tests and mocks, Docker, Docker-compose, scalability, distributed systems, and writing effective tests and code.

In one project, I created a resilient, reliable, and scalable architecture for the company's crawler, which allowed the system to crawl, parse, and process 100K finance reports in less than 2 hours, 4x faster. Before my solution, the crawler took more than 8 hours to run, which caused problems for the finance team. This new architecture is event-driven and written in Python, with Celery as a task queue/job. The new architecture enabled the company to scale the crawler into many machines while still using the same resources and providing better confiability, observability and logs.

In another project, I led the correction and improvement of the codebase for a web application known as player tracking, developed in Python and Django. I removed duplicate code, reduced complexity, and improved code coverage and architecture, making the product more stable and reliable.

I also implemented a culture of testing, containers, and code quality, using reports about code quality, such as cyclomatic complexity, to identify areas that required refactoring.

Responsibilities:

• Product roadmap definition with stakeholders

• Lead and mentor senior and software engineers

• Create resilient and scale architectures

Most Used Technologies:

Data: PostgreSQL, MySQL, MongoDB, Redis.

Task queue/job queue: Celery.

Message broker: RabbitMQ.

Languages: Python.

Frameworks web: Flask, Django.

Infra: Docker, Docker-Compose, AWS, Linux, Fabric, Jenkins.

I successfully optimized the performance of a critical PostgreSQL table containing 1 billion records, which was a significant bottleneck for the system. After thoroughly investigating the issue, I identified and implemented a better index for the slowest query, resulting in an 85% reduction in the query's P99 request time (6x faster). Moreover, I optimized the table storage by keeping only the necessary indexes, leading to a 73% reduction in storage space. My additional contributions helped ensure the system could handle the high volume of data without performance issues, significantly improving the overall user experience.

Most Used Technologies:

Data: *SQL, PostgreSQL, ElasticSearch, MongoDB, Redis.

Background Jobs: Sidekiq.

Languages: Ruby, Lua.

Frameworks: Rails.

Infra: Docker, AWS, Linux.

Tools: Git, Github.

I developed RESTful APIs in Python and Django using the Restkiss library, which was consumed by the front-end team. I wrote extensive unit tests to ensure the API's behavior and maintain code quality. These APIs were essential to the system, and my work contributed to the project's overall success.

I also contributed to open-source libraries. In the library Haystack, I refactored an existing pull request using object orientation, which reduced approximately 500 lines of code. In the Simple-history library, I submitted a pull request that allowed users to choose fields and reduced the amount of data stored, resulting in a more efficient and effective library.

Most Used Technologies:

Data: *SQL, PostgreSQL, ElasticSearch.

Task queue/job queue: Celery.

Languages: Python.

Frameworks: Django.

Infra: Docker, Vagrant, Heroku, Travis CI, Linux.

Tools: Git, Github.