Braze Integration and Cross-Platform Engagement Strategy
Brainly — Product Owner
Brainly needed a robust engagement automation platform to support scalable user communication across push notifications, in-app messaging, and email — all localized and behavior-triggered across platforms.
The engagement stack was outdated, non-personalized, and inefficient to scale across Brainly’s 350M+ user base.
The opportunity was to integrate Braze and establish a modern, fully owned user engagement pipeline — covering push notifications, in-app messaging, and lifecycle automation with high segmentation precision.
- Led vendor research, evaluation, and contract negotiation, aligning stakeholders across Product, Marketing, and Legal.
- PMed the full integration across Android, iOS, and Web, managing cross-functional dependencies (backend, localization, frontend, analytics).
- Designed scalable user journeys for push and in-app, mapped to behavioral triggers across the user lifecycle (onboarding, activation, re-engagement, upsell).
- Post-integration, assumed full ownership of the Braze channel: campaign strategy, message design, testing, and performance optimization.
- Implemented A/B and multivariate testing for timing, messaging tone, and feature prompts.
- Trained internal teams on Braze usage, permissions, segmentation logic, and lifecycle program structure.
- Completed Braze integration across all platforms under time and scope constraints.
- Decreased campaign creation time by over 80%, from multi-week engineering cycles to self-service execution.
- Boosted push and in-app engagement metrics through highly targeted journeys tied to user behavior.
- Enabled market- and language-specific personalization at scale.
- Maintained full product-side ownership of the channel post-launch, aligning messaging strategy with product and monetization goals.
Crowdin Integration and Localization Pipeline Ownership
Brainly — Product Owner
Brainly’s localization process was inefficient and error-prone, relying on outdated tooling (OneSky), manual developer requests, and inconsistent translation quality across platforms and markets.
With a global user base and content-driven platform, localization bottlenecks were delaying releases and creating UX inconsistencies.
The opportunity was to fully modernize the localization pipeline by selecting a better tool, automating developer workflows, and building a scalable internal localization operation.
- Researched and compared localization platforms; selected Crowdin based on automation potential, scalability, and platform compatibility.
- Led vendor negotiation, internal approval, and integration project across Android, iOS, and Web platforms.
- Owned post-integration management of the localization channel and acted as the single point of contact across development, product, and content teams.
- Selected, hired, and managed the internal localization team (translators, reviewers, language leads).
- Built automated workflows for developers to push and pull localization updates directly through Crowdin pipelines, removing manual steps.
- Designed process governance: versioning, priority setting, fallback handling, and communication protocols.
- Trained internal teams (engineering, content, product) on Crowdin workflows and access management.
- Replaced OneSky with Crowdin, enabling real-time, version-controlled localization across all platforms.
- Reduced localization turnaround time by eliminating manual developer steps and content handoffs.
- Improved translation consistency and quality across languages and product experiences.
- Established a repeatable, scalable localization framework adaptable to new products and markets.
- Increased engineering satisfaction by eliminating friction in the translation flow.
Django-Based Collaborative Dissertation Platform
Freelancer — Systems Architect
Traditional academic supervision tools (Word, email, PDFs) were unstructured, hard to track, and inefficient for managing feedback on a 300+ page dissertation under constant revision.
The goal was to build a purpose-built collaboration platform for real-time document review between PhD student and advisor.
Academic advisors needed a structured way to comment on specific sections, track changes over time, and reference data tables and figures — none of which was well-supported by traditional tools.
The opportunity was to build a lightweight but powerful Django-based app that enabled inline feedback, review tracking, and custom integration of code-generated content.
- Designed a custom collaboration platform tailored to the academic supervision workflow, enabling structured feedback and version control across long-form documents.
- Built a local-first system that prioritized data privacy, modularity, and full author control over content and revisions.
- Developed flexible editing and commenting capabilities to support granular, sentence-level review and iterative improvement.
- Implemented dynamic content handling to support integration of external elements such as data tables and figures generated in Python.
- Created administrative tools to manage documents, track feedback cycles, and support academic process transparency.
- Future-proofed the architecture to accommodate citation logic, content compilation, and extended use beyond a single dissertation.
- Eliminated friction in PhD feedback loops — supervisor could leave comments per sentence, reducing back-and-forth confusion.
- Enabled centralized review across dozens of chapters, interviews, figures, and datasets.
- Significantly reduced cognitive load for managing revision cycles and integrating comments.
- Demonstrated feasibility of a structured alternative to traditional word processors for academic research.