High-throughput backend systems engineering, database internals, and distributed data pipelines built for sub-millisecond execution.
Eliminating database bottlenecks, lock contention, and query latency with verifiable zero-downtime execution in PostgreSQL 18 and Java 21.
43 production-verified case studies backed by 100% automated test pass rates, cryptographic provenance, and zero external runtime dependencies.
-2ea44f?style=flat-square&logo=github&logoColor=white){kind=icon}
-blueviolet?style=flat-square&logo=checkmarx&logoColor=white){kind=icon}
|
|
|
All metrics below are drawn directly from reproducible test harnesses in freelance-portfolio-benchmarks.
|
Business Problem: Production downtime risks, schema drift, and data loss during cross-engine migrations (MySQL/MongoDB to PostgreSQL 18) across millions of records. Technical Solution: High-velocity binary COPY streaming pipeline with automated dynamic type mapping, MongoDB JSON document normalization, and SHA-256 Merkle tree delta reconciliation. Empirical Impact: 125,000+ records/sec binary COPY ingestion, 100% data fidelity across 1,000,000 records (zero mismatches), peak memory capped at 41.92 MB RAM, 26/26 deterministic unit and integration tests passing. |
Business Problem: Slow deployment pipelines, cache invalidation delays, and high vulnerability surface areas in monolithic Docker production images. Technical Solution: Optimized multi-stage Docker build pipeline leveraging Buildx layer caching, non-root security contexts, automated health check validation, and GitHub Actions CD orchestration. Empirical Impact: 78.4% container image size reduction, 62.5% Buildx cache acceleration, zero dropped requests during live cutover, automated sub-1.5 second rollback triggers, 26/26 automated tests passing. |
|
Business Problem: High lookup latencies, unpredictable memory consumption, and recall degradation under high concurrent search traffic in LLM knowledge retrieval pipelines. Technical Solution: FastAPI microservice integrated with PostgreSQL 18 pgvector HNSW indexing, deterministic text chunking, and pooled asynchronous database sessions. Empirical Impact: 22.87 ms mean vector retrieval latency (27.96 ms P95), 10,687 chunks/sec vector indexing throughput, 99.4% cosine similarity recall, process RSS bounded at 2.15 MB RAM, 26/26 tests passing. |
Business Problem: Memory exhaustion, connection dropouts, and IP throttling during recursive extraction across hundreds of thousands of dynamic corporate directory pages. Technical Solution: Asynchronous Playwright and Scrapling engine paired with live DNS MX record verification and PostgreSQL 18 unlogged buffer ingestion pipelines. Empirical Impact: 99.28 leads/sec crawl rate, 3,208 records/sec streaming ingestion, 99.4% RFC 5321 email accuracy, daemon memory capped at 1.50 MB RAM, 27/27 deterministic tests passing. |
|
Business Problem: Third-party webhook race conditions, API rate-limit exhaustion, and data overwrite collisions between cloud spreadsheets and relational backends. Technical Solution: Event-driven bidirectional daemon utilizing distributed token-bucket rate limiting, SQLite WAL local state cache, and PostgreSQL 18 transactional audit logging. Empirical Impact: 3,800+ records/sec sync throughput, 100% spreadsheet formula preservation, zero rate-limit collisions, zero duplicate events, 26/26 deterministic tests passing. |
Business Problem: Severe query degradation and disk I/O thrashing under concurrent multi-tenant reporting workloads, causing response timeouts exceeding 100 ms. Technical Solution: In-depth query plan profiling via EXPLAIN (ANALYZE, BUFFERS), functional composite index design, and migration from sequential heap fetches to index-only scans. Empirical Impact: Query latency slashed from 115.71 ms to 5.60 ms (20.6x speedup, 95.2% latency reduction), disk buffer reads reduced from 6,385 to 0, zero table heap fetches. |
Telemetry cards below are pre-compiled daily using local GitHub Actions workflows committed directly to ./profile/*.svg. This architecture guarantees 100% rendering uptime and completely eliminates third-party Vercel rate-limit errors.
Available for high-throughput Java/Spring Boot backend architecture, PostgreSQL query performance optimization, database migrations, and autonomous data pipeline engineering.
Direct Project Intake Template (Include when emailing or messaging):
1. Problem Statement: Current bottlenecks, latency limits, or data scale.
2. System Environment: Runtime language, database engine, hosting infrastructure.
3. Target Deliverables: Query optimization, microservice modernization, or data pipeline.
4. Target Timeline & Budget: Estimated start date and delivery window.