Diogo Martins is a performance marketing leader with 11 years of experience scaling user acquisition and growth across gaming, media, and lottery apps, currently leading Performance Marketing at Autopilot. He has managed seven-figure monthly budgets and multi-channel campaigns (Google, TikTok, Meta, CTV, programmatic partners) while partnering closely with product, creative, and analytics teams to drive profitable expansion. At DraftKings/Jackpocket and Tilting Point he translated creative insights and A/B learnings into measurable CTR and CPI improvements and built dashboards to inform C-level decision-making. He pairs a data-first approach with hands-on creative testing—once lifting CTR by 40% and reducing CPI by 30% for a title—while keeping an eye on monetization levers that increased partner revenue by 30%. Multilingual and internationally educated (Nova SBE, Manhattan Institute of Management), he blends cultural fluency with analytical rigor and even contributes to open-source testing for Python libraries, reflecting a detail-oriented engineering mindset. Based in Rio de Janeiro, he’s known for curiosity-driven growth strategies and an appetite for ambitious, cross-functional challenges.
11 years of coding experience
4 years of employment as a software developer
International management, International management at Manhattan Institute of Management
Master's degree (#17 by the Financial Times rankings), Management, Master's degree (#17 by the Financial Times rankings), Management at Nova School of Business and Economics
Python client library for Mesos Marathon's REST API
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:5 commits, 1 PR, 4 comments in 1 day
Contributions summary:Diogo focused on testing functionality within the `marathon-python` client library. Their contributions involved adding and implementing comprehensive unit tests, specifically for the `MarathonConstraint` model. This included testing representation methods, JSON serialization/deserialization, and the `from_string` class method. The user also refactored existing code and removed whitespace, further ensuring code quality and maintainability.
Amora Data Build Tool enables analysts and engineers to transform data on the data warehouse (BigQuery) by writing Amora Models that describe the data schema using Python's "PEP484 - Type Hints" and select statements with SQLAlchemy. Amora is able to transform Python code into SQL data transformation jobs that run inside the warehouse.
Contributions:36 releases, 118 reviews, 950 commits in 1 year 4 months
bigquerydata-warehousepythonsqlsqlalchemy
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