High-leverage data systems with product-grade execution.

Senior software engineer at Apple building high-leverage data tooling and analytics systems. I design query semantics, interaction systems, and delivery pipelines that feel intentional end-to-end.

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Current Role



Apple

Senior Software Engineer · October 2022 to present

Building schema-aware data tooling, modular grid systems, and, amongst other things, the connective tissue around them.

Domains

Data PlatformsInteractive GridsQuery EnginesDeveloper ExperienceAutomationApp Architecture

Highlights

λCurrent focusModular table platform with schema-aware intelligence

σActive output3 featured notes

§02Credibility Strip

BSc Computer Science, Trinity College Dublin. MSc Computer Science (part-time), University College Dublin, with an ML/AI focus including GOFAI.

Senior Software Engineer at Apple (October 2022 to present).

Founder/CTO experience scaling product and engineering teams (2013-2022).

Delivered regional (EMEIA) and worldwide programs with cross-region stakeholders.

Built and operated large-scale internal systems across data, automation, and platform workflows.

Dad of a one-year-old and a three-year-old. Food-throwing is trending down. I'm calling that progress. 👨 👶

§03Capability Matrix

Systems thinking translated into shippable architecture.

ΔQuery Systems

Semantic query contracts that scale with product complexity

Schema-aware filtering and aggregation

Server-side performance by default

Predictable contracts from UI to storage

∇Frontend Systems

High-density interfaces with product-level fit and finish

Component architecture for table-heavy workflows

Interaction models tuned for speed

Design language that preserves technical clarity

∫Delivery

Engineering process that trades drama for reliable shipping

Test strategy focused on real regressions

Documentation designed for team handoffs

Automation used where it removes toil

§04Selected Output

Featured Writing

Technical notes

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2026-03-1813 min read

⊘Sanitized

Setgraph, Apple Health, TabPFN, and the day my lifting log got serious

I pulled my Setgraph history and Apple Health context into one table, rebuilt 1,548 exercise-sessions, benchmarked six tabular models, and learned three things fast: completed session quality was more explainable than I expected, cardio 24-48 hours before lifting looked better than expected, and personal-record windows were narrower than normal training defaults.

machine-learning fitness tabular-ml experiments

4 min read

The Loop Is the Product

Ralph loops, VS Code Autopilot, Chief, and Lisa all point at the same thing: the useful part is persistence, not the prompt.

6 min read

Why VS Code Insiders Feels Worth It Again

My take on recent VS Code Insiders and agent workflow updates, based on public sources, with a few reasons to keep Stable installed too.