Robust data substrates and ML/LLM systems engineered for production invariants: lineage, idempotency, observability and evaluation under distribution shift.
Pine Marten is the independent practice of John Houghton, a senior data engineer with ~10 years spanning Meta, Apple, Kaiser Permanente and Intuit. I design ingestion and transformation DAGs, semantic/metrics layers and ML/LLM systems — including on-prem inference with a data-residency boundary at your physical perimeter, eliminating third-party exfiltration surface.
Capabilities
Meta quantum cryptography systems
PQC migration for production data systems: exhaustive cryptographic bill-of-materials across key hierarchies and pipeline transport/at-rest layers, crypto-agility assessment of algorithm-binding surfaces, and harvest-now-decrypt-later threat modeling relative to the confidentiality lifetime of assets, targeting module-lattice ML-KEM (FIPS 203) and ML-DSA (FIPS 204).
ML-KEM / ML-DSAcrypto-agilityHNDL riskkey inventoryData engineering
ELT architectures with declarative dbt transformation graphs, asset-oriented orchestration, contract and data-quality tests in CI, engineered for low bus-factor transfer of ownership to your team.
dbtDagsterDuckDBSQLAI engineering
LLM-backed features and tool-augmented agents embedded in live workflows, with offline/online evaluation harnesses, curated datasets and policy guardrails instrumented from inception rather than retrofitted.
LLMsagentsevalsLocal & private AI
On-device ASR/TTS, vision and language-model inference on Apple Silicon unified-memory systems or self-hosted accelerators, with a strict no-egress data boundary.
on-premSTT/TTSvisionAnalytics & decision support
Metric specification, causal experimentation and decision dashboards grounded in economic reasoning, so estimands map directly onto the decisions leadership must make.
metricsexperimentationSmall, senior, hands-on
Assess
A time-boxed, fixed-scope assessment of data assets and objectives, culminating in a written, prioritized roadmap.
Build
I operate within your repositories and toolchain, shipping small, reviewable increments with test coverage and documentation.
Hand off
Operational runbooks, knowledge-transfer sessions, and a team capable of extending the system without consultant dependency.
TODO
Hi, I'm John.
My career has centered on converting heterogeneous, noisy data into reliable systems — most recently supporting FAIR and Reality Labs at Meta, and previously Apple Maps, Kaiser Permanente and Intuit. Economics training orients me toward the decision-theoretic value of data rather than pipeline throughput alone.
Have a data or ML problem with real decision value?
Send a brief problem statement and context. I respond within one business day.