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GameDuo

DEV Team Server Developer

2025.01 — Present

Mobile game development and publishing company. Shared backend platform design for 12 live services, data pipeline engineering, and common package system operations

Highlights

  • Marketing data query cost surge (full-scan billing model) — Evaluated partitioning but rejected due to Write IOPS increase; adopted gRPC distributed reads (Storage Read API), Cut costs 82% ($6.25→$1.1 per TB)
  • Batch processing collection limits (60 days) — Migrated to serverless architecture (SQS+Lambda+EventBridge), Expanded metrics range 6x (60→360 days), processing time 2h→5min
  • Code duplication and convention inconsistency across 7 projects — Established shared package system (NestJS utility 10 modules + Game Server Kit 2 packages), Automated upgrades cutting deployment time 3hrs→15min
  • Marketing query latency (18s) from fragmented ad platform data (Google Ads/Meta/TikTok) — Table normalization and index tuning, Performance improved 97% (18s→0.5s)
  • Regulatory risk from game probability disclosure requirements — Integrated DynamicModule-based probability package across multi-game projects + CDK-based audit log pipeline, Achieved 94%+ integration test coverage (12 suites, 115 tests)

Projects

Glider Sheet Audit Log System

2025.01 ~ 2025.04

Resolved delayed balance issue response caused by inability to track data change history during game operations

  • Inability to track data change history across environments and projects — Designed Git-like version control system with UUID-based cross-environment/project entity tracking, Ensured data consistency across 6 games
  • Manual entity change recording prone to omission — Applied Event Sourcing-based change tracking with Auditable decorator + TypeORM Subscriber pattern, Automated entity change recording pipeline
  • Multi-environment version conflicts during data merge — Developed 3-Way Merge Engine with parent/child entity conflict detection and unique constraint handling, Enabled reliable version merging across 6 games
  • Expensive full-snapshot comparison for every version diff — Designed Version Diff Engine with dual strategy: incremental comparison and snapshot comparison based on Base Audit availability, Established cost-efficient version diffing
  • Entity tracking failures due to PK dependency during migration and merge — Introduced shared identifier-based entity tracking decoupled from PK dependency, Ensured accurate entity tracking during migration, comparison, and merge

Tech Rationale

Adopted TypeORM EntitySubscriberInterface after dedicated analysis of subscriber behavior and constraints. Designed AOP-based approach combining Auditable decorator + Subscriber to automatically collect entity changes into a standardized audit pipeline.

TypeScript NestJS TypeORM MySQL

Marketing Integrated Platform

2025.02 ~ Present

Unified marketing platform managing Google Ads/Meta/TikTok campaigns, creatives, and metrics in a single system, built on dedicated marketing DB separation

  • Fragmented ad platform management across Google Ads/Meta/TikTok — Delivered unified campaign automation API for creation, deployment, modification, and retention metrics in a single platform, Consolidated multi-platform campaign management into one system
  • NAS-S3 sync reliability and restore failures — Separated SQS server-worker flow, automated outbox events, tracked asset_nas_sync state, Secured restore reliability with state-tracked sync pipeline
  • Meta asset sync performance bottleneck — Converted ORM single-row saves to bulk processing, Image sync reduced 72% (25.7s→7.1s), DB transactions reduced 95% (10~16s→0.3~0.5s)
  • High creative processing Lambda costs on x86 architecture — Migrated Lambda to ARM64 Graviton2 architecture, Reduced creative processing Lambda costs by 20%
  • High BigQuery query costs for marketing data reads — Adopted BigQuery Storage Read API with gRPC streaming-based high-performance data reading, Reduced BigQuery costs by 82% ($6.25→$1.1 per TB)
  • Marketing query latency (18s) from 100+ column denormalized table — Normalized table into main/time-series/prediction, tuned indexes + cursor pagination, Performance improved 97% (18s→0.5s)

Tech Rationale

Reduced BigQuery costs 82% via Storage Read API migration. Designed hybrid GCP Pub/Sub → AWS Lambda/SQS pipeline for event-driven processing with Outbox pattern for non-blocking async event publishing.

AWS Lambda Apache Arrow BigQuery GCP Pub/Sub Google Ads API Meta API MySQL NestJS S3 SQS BigQuery (Storage Read API) TikTok API TypeORM TypeScript gRPC

AWS Lambda Migration & Event-Driven Architecture

2025.06 ~ 2025.08

Resolved batch job limitations from marketing metrics 60-day → 360-day expansion and serverless transition for batch processing

  • Full serverless migration risked operational stability — Designed hybrid architecture keeping API server on EC2 while separating batch/job processing to Lambda, Attained workload-optimized resource usage without disrupting live services
  • Batch processing limited to 60-day collection range with 2-hour runtime — Established Event-Driven flow with SQS+Lambda+EventBridge, Reduced batch time 2h→5min, expanded collection 6x (60→360 days)
  • Data consistency risk during event publishing — Adopted Transactional Outbox Pattern for scheduled and delayed event publishing, Ensured data consistency across distributed event processing
  • Lambda throttling, high log costs, and build OOM issues — Applied Batch Size bulk processing, CloudWatch log optimization, and build OOM remediation, Stabilized Lambda operations and reduced operational costs
  • DB connection exhaustion during massive Lambda execution — Introduced RDS Proxy connection pooling, Resolved connection exhaustion and stabilized database access

Tech Rationale

Chose Lambda to decouple batch/event workloads bound to the monolith server, enabling independent deployment. Adopted SQS for async processing to resolve scaling limitations under traffic fluctuation.

TypeScript NestJS AWS Lambda SQS SNS EventBridge RDS Proxy AWS CDK

Cloud Data Sync System

2025.08 ~ Present

Built S3-based sync and automated DDL management system to resolve dynamic game data inconsistency across environments

  • Dynamic game data inconsistency across environments — Built S3-based cross-environment data synchronization across development/staging/production, Unified game data state across all environments
  • Manual DDL schema management causing sync failures — Created automated DDL management engine with dynamic PK column type resolution, column type mismatch detection with MODIFY, and automatic index creation/RENAME, Automated schema sync across environments
  • Large-scale Cloud Data ingestion and S3 upload bottlenecks — Analyzed and optimized ingestion and upload pipeline, Resolved large-scale processing delays
  • Sync job instability causing operational issues — Implemented job separation, transitioned scheduling approach, tuned timeouts, and introduced non-blocking processing, Secured operational reliability for sync pipeline
  • Four CloudData bugs: Redis cache invalidation, DDL SKIP metadata, copy key deletion, excludeCloudData propagation — Diagnosed and fixed all four issues systematically, Stabilized CloudData operations
  • Unintended full Cloud Data deletion risk from missing option forwarding — Added excludeCloudData option forwarding to four POST migration paths, Blocked unintended data deletion risk

Tech Rationale

Applied S3 Lifecycle policies (30d Glacier IR, 90d expiry) for cost optimization. Switched from event-triggered to scheduled execution to reduce sync miss risk.

TypeScript NestJS TypeORM MySQL S3

Internal Common Library System

2025.07 ~ Present

Development and operation of NestJS utility (10 modules) + Game Server Kit (2 packages) for multi-project code consistency

  • Common code duplication across services increasing maintenance cost — Designed 10-module library system: core, repository, cache, lock, slack, crypto, smb, hash, type, iac, Unified shared code across 7 projects with standardized dependency management
  • Repository module lacking bulk operations and audit logging capabilities — Enhanced Repository module with Bulk/Audit Log/TypeORM narrowing using overloading + TypeScript generics; refactored 2000+ lines by SRP, Improved module extensibility and maintainability
  • Concurrency conflicts in multi-instance environments — Engineered distributed lock module with ElastiCache (Redis)-based distributed lock decorator, Enabled safe concurrency control across multi-instance deployments
  • Manual library upgrades taking 3 hours across 7 projects — Added workflow_dispatch+matrix and changed-package CI tests on GitHub Packages, Reduced 7-project upgrade runtime 3h→15min
  • Game server shared code tightly coupled in monolithic package — Extracted game-server Kit: moved Sheet+5 submodules to @gameduo/glider-sheet, resolved conflicts via 5-branch divergence analysis, Realized independent package versioning and deployment
  • Slow CI pipeline (15m47s) due to test infrastructure inefficiency — Switched ts-jest isolatedModules and explicit Entity types, Cut CI time 61% (15m47s→6m06s); passed 81 suites/978 tests

Tech Rationale

Implemented ElastiCache (Redis) distributed lock as AOP decorator to separate lock logic from business code. Evaluated 5 options for Jest 30 VM isolation and adopted poolSize=2.

ElastiCache (Redis) GitHub NPM NestJS Slack API TypeORM TypeScript

Probability Calculation & Audit Log Analytics Pipeline (Glider Probability)

2026.02 ~ Present

Built probability calculation package and CDK-based audit log analytics infrastructure to address regulatory risk from lack of game probability verification

  • No reusable probability calculation module across game projects — Packaged NestJS DynamicModule with 5 functions + Kinesis logging as @gameduo/glider-probability, Integrated into 12 games with standardized probability calculation
  • No audit log analytics infrastructure for probability verification — Codified CDK analytics pipeline: Kinesis→Firehose (Dynamic Partitioning+Parquet)→S3→Glue→Athena, Unlocked end-to-end probability audit log querying
  • Mock-based tests lacking regression confidence for infrastructure code — Replaced mocks with LocalStack + Testcontainers integration tests, Secured 94%+ coverage (12 suites/115 tests)

Tech Rationale

Codified Kinesis → Firehose (Dynamic Partitioning + Parquet) → S3 → Glue → Athena pipeline with CDK. Chose Parquet columnar format for Athena SQL cost optimization. Replaced mock-based tests with LocalStack Testcontainers for integration testing.

Contribution: Restructured changmin's initial implementation into a package. Independently handled P0 bug fixes, test stabilization (94%+ coverage), CDK infrastructure, and deployment across 12 projects.
TypeScript NestJS AWS CDK Kinesis Firehose S3 Athena Glue Parquet LocalStack Testcontainers

Activities

Boosting Developer Productivity with Amazon Q Developer

Conference Talk
2025.10

Games on AWS 2025 Customer Session

Shared a case study of a single engineer scaling data pipeline capacity 270x in 10 days, measured by hourly concurrent job throughput

Technical Skills

Node.js TypeScript NestJS TypeORM MySQL ElastiCache (Redis) BigQuery Apache Arrow gRPC AWS Lambda SQS SNS EventBridge S3 RDS Proxy AWS CDK GCP Pub/Sub Ad Platform APIs (Google, Meta, TikTok) Slack API GitHub NPM Kinesis / Firehose Athena Datadog