Founder · technical consultant

Make complex systems simpler to run.

I’m Wayne. My background is in Information engineering, production support and cloud reliability. I now apply the same operating discipline to automation: clear ownership, useful monitoring, recoverable failure paths and less manual work.

See workflow work ↓ View example builds ↗

Why this work

Automation is only useful when people can trust it.

Years in production database engineering taught me that the happy path is the easy part. The real work is knowing what happens when data is late, an API fails, a credential expires, a query slows down or ownership is unclear.

I have worked across PostgreSQL, Oracle, MySQL, MSSQL and Sybase environments, with a strong focus on performance, availability, troubleshooting and operational reliability.

At Salesforce Cloud Infrastructure, that included automation, workflow and cloud database operations, Python/Flask automation, CI/CD modernisation, observability and production troubleshooting across AWS and hybrid platforms.

Before that, at Allied Irish Banks, I worked across Linux, HP-UX and IBM mainframe environments, including performance tuning, security, automation and an Oracle Platform-as-a-Service private cloud.

That background shapes how I approach workflow consulting today: understand the operational risk first, then use the simplest tool that can solve the problem cleanly.

Workflow builds

Different tools. Same production mindset.

I have created workflows with n8n, Apache Airflow and Python. The tool changes with the problem; the design priorities do not: visible state, sensible error handling, controlled dependencies and a clear operator.

n8n workflow automation

Connect business systems, move data, trigger actions and keep manual handoffs out of the critical path.

⚡Trigger

→

✓Validate

→

⇄API / App

→

🔔Notify

Typical pattern: receive an event → check the data → update another system → log or notify the owner.

Best fit

Cross-system business workflows where clarity, fast iteration and maintainable integrations matter.

Apache Airflow orchestration

Coordinate scheduled or dependency-driven work where order, retries and execution visibility matter.

◷Schedule

→

⇣Extract

→

⚙Process

→

▣Load

Typical pattern: schedule a DAG → run dependency-aware tasks → retry controlled failures → expose task state to operators.

Best fit

Data and operational pipelines that need explicit dependencies, repeatable schedules and clear execution history.

Python automation

Use code when the workflow needs custom logic, deeper system access or tighter control than a visual tool provides.

⌘Collect

→

⌁Analyse

→

!Detect

→

↗Act

Proven use: Python/Flask automation for database monitoring, performance analysis and error detection.

Best fit

Custom operational tooling, database automation, diagnostics and logic that needs precise behaviour.

Operating experience

Built in production, not only in demos.

The founder perspective comes from years of operating systems where performance, recovery and change control have real consequences.

2013 — 2025

Lead Database Engineer

Salesforce Cloud Infrastructure

PostgreSQL, Oracle and MySQL across AWS and hybrid platforms; Using Stackstorm workflows, performance troubleshooting, Python automation, CI/CD, observability and reliability work.

2007 — 2013

Senior Database Administrator

Allied Irish Banks (AIB)

Oracle, MSSQL and Sybase across Linux, HP-UX and mainframe environments; platform engineering, security, automation and performance baselining.

Performance

Improved PostgreSQL query performance by 35% through index optimisation and slow-query tuning.

Automation

Reduced manual DBA workload by 40% by automating backups and health checks.

Delivery

Reduced schema-change release time by 50% through Jenkins CI/CD modernisation.

Cost

Reduced operational costs by 25% through database consolidation and automation.

Technical foundation

Broad enough to see the whole operating path.

Automation rarely lives alone. Databases, cloud services, CI/CD, credentials, monitoring and recovery all affect whether a workflow is dependable in production.

Core technology

PostgreSQL administrationAWS RDSSQL optimisationIndexingHigh availabilityBackupsPythonBashSQLn8nApache AirflowJenkinsTerraformAnsibleGrafanaPrometheusSplunkLokiOracleMySQLMSSQL

Next step

Bring one workflow that should be easier to operate.

Start with the process, the systems involved, where it slows down or fails, and who needs to own the result. The technology comes after that.

Return to Strategy Guide Win ↗ Explore example builds ↗