Work
Case studies.
Technical stories from real production systems. SiteRevo as a product in development,
current data architecture work, and anonymized engineering patterns from database and
infrastructure projects.
Earlier in my career I built custom websites and web applications for small businesses.
Those projects are no longer the focus — the work below reflects the current positioning.
Product in Development In Development
Building SiteRevo
Founder, architect, and lead engineer
SiteRevo is my own product, currently in active development. It is the anchor
proof point for my product engineering and founder work — built on Laravel, AWS, and a
highly available database architecture designed to handle reporting-heavy SaaS workloads.
The problem being solved
SiteRevo addresses a gap in inspection, reporting, and field operations tooling —
where existing solutions are either too heavyweight for smaller operators or too generic
to handle the nuances of structured field data, report generation, and customer-facing outputs.
Architecture choices
Built on Laravel with a well-defined service layer, event-driven background processing
via queues, and a PostgreSQL/Aurora backend. The infrastructure runs on AWS with deployment
automation, read replica routing, and backup/restore procedures tested on a regular cadence.
AI integration
AI-assisted features are baked into the product design from the start, not bolted on.
Extraction, summarization, and report drafting are informed by a structured evaluation
approach to keep outputs consistent and measurable across product updates.
What building it demonstrates
Product decisions under resource constraints, architecture tradeoffs with operational
consequences, and the operational habits required to keep a growing system reliable —
backup testing, migration strategies, deployment safety, and monitoring coverage.
Laravel PHP AWS Aurora PostgreSQL DevOps AI SaaS
Current Role Active
Data Architecture at Sycle
Data Architect
At Sycle I work as Data Architect, responsible for the data infrastructure
that underpins a production SaaS platform serving real customers. The work is hands-on
and spans architecture, reliability, and cross-functional collaboration.
Data architecture responsibilities
Designing and evolving the data models that support product features, reporting,
and integrations. This includes schema design, normalization tradeoffs, and ensuring
data integrity across a complex, multi-tenant system.
Database reliability and performance
Query tuning, index strategy, migration safety, and monitoring for the production
database fleet. Establishing thresholds, alert runbooks, and the operational cadence
needed to catch issues before they become incidents.
Cross-functional engineering
Working with product and engineering teams to translate product requirements into
sound data models, and translating infrastructure constraints back into product decisions.
Technical communication is a core part of the role.
Data Architecture PostgreSQL MySQL AWS SaaS Reliability
Engineering Story Ongoing
High Availability Database Work
Senior database engineer
Production database reliability work accumulated across multiple systems
and environments. The following is an anonymized summary of recurring patterns and outcomes.
Cluster architecture
Aurora MySQL and PostgreSQL with writer/reader topologies. Routing read-heavy
workloads to replicas, managing lag thresholds, and building connection pooling strategies
that do not break under load spikes.
Backup and restore
Automated snapshot policies, point-in-time recovery configuration, and restore
testing on a regular cadence. The goal is not just having backups — it is knowing they
work before you need them.
Migration safety
Zero-downtime schema migrations using pt-online-schema-change and similar tooling.
Rolling deployments that do not require maintenance windows. Pre/post migration checklists
that catch column type changes, missing indexes, and foreign key issues before they ship.
Incident prevention
Disk space, connection count, lock wait, and slow query monitoring with actionable
alert thresholds. Runbooks that can be followed by on-call engineers who did not write
the system. Post-mortems that change behavior, not just document it.
Aurora MySQL PostgreSQL RDS Replication Backups Monitoring
Engineering Story Ongoing
Backend Platform & DevOps Automation
Backend and infrastructure engineer
Deployment pipelines, environment management, observability, and infrastructure
modernization work from production systems. Anonymized where needed.
CI/CD pipelines
GitHub Actions workflows with automated test gates, staging deployments, and
production promotion flows. Blue/green deployment strategies on AWS to eliminate downtime
during releases and simplify rollback.
Queue workers and job processing
Laravel Horizon and SQS-backed queue workers with supervisor configuration, restart
strategies, and job failure monitoring. Dead letter queue routing and retry policies
designed for specific business rules rather than generic retry counts.
Environment management
Production/staging parity as a discipline: same services, same config shape,
secrets via AWS Secrets Manager or Parameter Store, no environment-specific code paths.
Observability
CloudWatch dashboards, metric filters, and log-based alarms. Application-level
structured logging routed to CloudWatch Logs Insights for query and post-incident analysis.
AWS GitHub Actions Laravel Horizon SQS CloudWatch Docker