Building high-scale systems

Senior Backend
Engineer

Specialized in high-scale financial systems. Building payment processing platforms, critical APIs, and secure backend infrastructure for production environments that cannot afford to fail.

10+
Years backend
engineering
PCI
DSS compliant
systems
Go
Python · PHP
core stack
View Experience Get in Touch
Alex Dominguez — Senior Backend Engineer
Alex Senior Backend Engineer · Fintech
GoPythonPHPLaravelMySQLRedisDockerPCI DSSREST APIsMongoDBLinuxFintechPayment SystemsBackend ArchitectureHigh AvailabilityGoPythonPHPLaravelMySQLRedisDockerPCI DSSREST APIsMongoDBLinuxFintechPayment SystemsBackend ArchitectureHigh Availability
About

Engineering at the intersection
of finance and scale

I'm a Computer Science Engineer with over six years building backend systems in high-stakes environments. My career has been defined by one consistent thread: working on systems where downtime has real financial consequences and where correctness is non-negotiable.

Starting with custom software solutions for clients across multiple industries, I progressively specialized toward financial technology — where the demands on backend engineering are most intense. Today, I work as a Senior Backend Developer at a fintech company, maintaining and evolving payment processing infrastructure that operates under strict security standards.

My current focus is Go and Python for new system development — leveraging Go for high-concurrency services and Python for automation, data pipelines, and tooling. I carry a deep foundation in PHP and Laravel from years of production use, which still serves me in maintenance, legacy systems, and architecture decisions.

"Production isn't a phase. It's the only phase that matters."

Current Role Senior Backend Developer Fintech · Payment Processing
Active Stack Go · Python · PHP/Laravel
Education BSc Computer Science Engineering
Languages
Spanish Native
English Intermediate (B2, in progress)
Focus Areas Payment Systems · API Design · Database Optimization · Security · High Availability
Tech Stack

Tools built for
production environments

Backend
Goactive
Pythonactive
PHP + Laravelfoundation
REST APIs
CodeIgniterlegacy
Databases
MySQLprimary
Redis
MongoDB
SQL Server
Infrastructure
Docker
Linux / WSL
CI/CD pipelines
PCI DSS
Domain
Payment Processing
API Integrations
ERP Systems
Androidintermediate
Experience

10+ years building systems

Oct 2019 — Present ~5.5 years
Pagadito
Fintech · Payment Processing Platform · Central America
pagadito.com ↗
Oct 2019 — Present ~5.5 years
Senior Backend Developer
Go Python PHP/Laravel MySQL Redis Docker PCI DSS
Design and maintenance of core payment processing services in Go — high concurrency, strict availability requirements, and deterministic failure handling.
Python-based automation and tooling for monitoring, reporting pipelines, and internal workflows that previously required manual intervention.
PHP/Laravel maintenance of legacy payment modules — bug fixes, security patches, and incremental migration toward Go services.
Security controls and compliance workflows aligned with PCI DSS standards across all active services.
Database query optimization and caching strategy — significantly reduced latency on critical transaction paths.
2018 — 2019 ~1 year
Jexan
Software company · Custom development · International clients
jexan.com ↗
2018 — 2019 ~1 year
Backend Developer
PHP Laravel Python MySQL REST APIs MongoDB CodeIgniter Android WordPress
Developed and delivered custom backend systems for international clients across logistics, retail, and services sectors using PHP and Laravel.
Built and maintained REST API integrations with multiple third-party services, handling authentication, rate limits, and error recovery.
Implemented ERP modules and internal control systems for mid-size businesses.
Android mobile applications integrated with backend APIs at intermediate level.
WordPress plugin development and CMS customization for client platforms.
Core Competencies

What I bring to
an engineering team

⬡
Backend Architecture
Designing service boundaries, data flows, and failure modes before writing a line of code. Systems that make operational sense at 10x current scale.
⬡
High-Performance APIs
REST API design built for real throughput — proper status semantics, versioning, pagination, rate limiting, and consumer-first documentation.
⬡
Database Optimization
Query profiling, index strategy, schema design for write-heavy workloads, and caching layers that prevent database bottlenecks from becoming business problems.
⬡
Secure Systems Design
Security as a structural concern — not an afterthought. PCI DSS compliance, input validation, secrets management, and secure data handling in financial contexts.
⬡
Production Debugging
Systematic approach to production incidents: log analysis, distributed tracing, reproducing intermittent failures, and root-cause resolution rather than symptom patching.
⬡
High Availability Systems
Designing for uptime: graceful degradation, retry logic, circuit breakers, and monitoring strategies for systems where unavailability means financial loss.
AI & Machine Learning

Applied AI from an
engineering perspective

Backend engineer with practical experience integrating AI-driven solutions into real-world systems. Focused on leveraging machine learning models and automation tools to enhance backend capabilities, optimize workflows, and enable intelligent features within scalable architectures.
◈
AI/ML API Integration
Integrating external AI/ML APIs into scalable backend services — handling authentication, rate limits, retries, and cost optimization as first-class engineering concerns.
◈
Async AI Workload Processing
Designing queue-backed systems that trigger, process, and respond to AI-generated outputs asynchronously — decoupling inference latency from user-facing response times.
◈
Workflow Automation with AI
Automating backend tasks using AI models within distributed systems — replacing manual, error-prone processes with observable, reliable pipelines.
◈
Local Model Execution
Running and optimizing AI models locally — managing CPU/GPU resource trade-offs, batching inference requests, and offloading workloads for resource-constrained environments.
◈
Production-Grade AI Pipelines
Evaluating performance, cost, and reliability trade-offs when deploying AI in production. Maintaining observability and fallback strategies so AI failures don't become system failures.
◈
Prompt Systems & Orchestration
Familiarity with prompt-based systems, AI-driven automation pipelines, and orchestration tools for distributed processing — combining AI capabilities with backend architecture patterns.
My approach to AI mirrors my backend background: production readiness, scalability, and measurable impact — not experimentation for its own sake.
Representative Projects

Systems built for
demanding environments

PROJECT / 01
High-Concurrency Transaction Service
Fintech · Go · Production
Problem
A legacy PHP payment component couldn't handle peak load without cascading failures. Race conditions in concurrent transaction processing caused financial inconsistencies that were hard to reproduce.
Solution
Rewrote the critical path in Go — leveraging goroutines with strict concurrency controls, idempotency keys, and explicit failure domains. Replaced ambiguous error states with a structured taxonomy mapped to financial reconciliation workflows.
GoMySQLRedisDocker
PROJECT / 02
Internal Automation Platform
Python · Data Pipelines · Tooling
Problem
Critical operational reports and monitoring tasks were entirely manual — slow, error-prone, and dependent on specific people being available.
Solution
Built a Python automation layer: scheduled pipelines for data aggregation and reporting, alert hooks for anomaly detection, and an internal CLI toolkit that reduced daily ops overhead significantly.
PythonMySQLRedisDocker
PROJECT / 03
Multi-Source Integration Layer
API Aggregation · Python + Go
Problem
Business logic depended on data from multiple external providers with inconsistent schemas, different auth models, and unpredictable uptime.
Solution
Built a unified integration layer with per-provider Python adapters, normalization contracts, circuit-breaker patterns, and fallback caching — insulating business logic from external volatility.
PythonGoRedisMongoDB
PROJECT / 04
Enterprise Resource Platform
ERP · PHP/Laravel · Internal Systems
Problem
A mid-size company running critical operations on fragmented spreadsheets and manual processes — reconciliation errors and audit liabilities were growing.
Solution
Designed and built a custom ERP backend in Laravel — role-based access control, automated reporting pipelines, and full audit trail logging compliant with internal control standards.
PHPLaravelMySQLSQL Server
Engineering Philosophy

How I think about
building software

01
Production is the spec
Every decision — schema changes, query choices, dependency upgrades — is evaluated against production load, production failure modes, and production recovery time. I don't separate "development mode" from "production mode" in my mental model.
02
Correctness before cleverness
Financial systems need to be provably correct, not impressively fast. I write code that is easy to verify, review, and debug at 3am during an incident — not code that wins elegance points in isolation.
03
Security is structural
Security controls bolted on after the fact are theater. I design systems with explicit trust boundaries, principle of least privilege, and compliance requirements as first-class architectural constraints.
04
Debugging is a skill
I invest in structured logging, meaningful observability, and the discipline to chase root causes rather than silence error messages. Good systems are debuggable by design.
05
Scale is a design problem
Scaling issues are almost always design issues discovered late. I think about data access patterns, concurrency, and bottleneck locations at architecture time — not when the system is already on fire.
"The goal is not to build software that works. The goal is to build software that keeps working."
// engineering.principles

const philosophy = {
  "correctness": "non-negotiable",
  "complexity": "earned, not assumed",
  "debugging": "a first-class skill",
  "security": "structural",
  "scale": "designed in",
  "boring": "a compliment"
};
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