I build AI-native data platforms — and multiply the teams that run them.
Matt Nelson · P.Eng · Berkeley MIDS · 10 years in data, 16 in engineering · Calgary, Canada
The path so far
2005–2010
University of Waterloo — BASc Chemical Engineering
2006–2010
Engineering Co-op — Lanmark · Olds Gas Plant · Pengrowth
2010–2014
Pengrowth Energy — Production & Exploitation Engineer
2014–2017
Centrica Energy — Exploitation / Development Engineer
2016–2018
Berkeley MIDS — Master of Information & Data Science
Master of Information and Data Science at UC Berkeley (3.89 GPA) — completed nights-and-weekends while working full-time. The pivot point where a decade of engineering turned into a data career.
2017–2022
Canlin Energy — Corporate Data Scientist & Reserves Manager
Drove the transition to self-service data: implemented Tableau company-wide (100+ data sources, 50 workbooks in year one) and owned the regulated annual reserves evaluation feeding audited financial statements.
2022–2024
Canlin Energy — Data & Advanced Analytics Lead
Designed, built, and ran Canlin’s modern data stack (dbt Cloud, Snowflake, DataRobot) and led IROC (remote operations centre) analytics — mentoring junior professionals into data roles along the way.
2024–2025
Paramount Resources — Senior Data Analytics Engineer
Senior IC on the Data Analytics and Integration team — corporate PowerBI dashboard suites, Streamlit optimization apps, and governance-level advocacy for a modern cloud data stack.
2025–2026
StackDX — Data Platform Engineer
Lead engineer on the US data platform rebuild: a legacy Python ETL stack replaced with a production Dagster architecture — 15 states, 945 transformation assets, a 3.8 TB source layer distilled to a 10.3 GB exposure layer serving 300M+ records — shipped AI-first with a custom Claude Code toolchain. Read more
2026–now
StackDX — Product Manager, Stack Maps & Public Data
Product Manager for Stack Maps and the Public Data team — a 6-person group across two countries — setting roadmap while still shipping in the codebases the roadmap depends on.
Flagship projects
2014
P.Eng designation — APEGA, 2014
Professional Engineer designation (APEGA) — the licensed-engineering rigor that underwrites every production system since.
2017–2022
Reserves evaluation ownership — +$730M technical revisions
Owned the externally-audited NI 51-101 reserves evaluation for five years — the board-level valuation of every corporate asset. Rigorous data and engineering work added +$730M (NPV10) in technical revisions. Read more
2021–now
twochannel.ai — 18k+ products · one engineer
Founder project: an AI-assisted HiFi catalog and system designer where LLM-in-the-loop pipelines maintain 18k+ products across 2k+ brands. The catalog is the size that would normally need a small data team behind it, and there is only me. Read more
2022–2024
dbt + Snowflake platform — Canlin’s data foundation
The dbt Cloud / Snowflake platform that became Canlin’s foundation for operational data, BI, and ML — automation from it saved 50+ hours per week across Operations. Read more
2022–2024
8,000-well forecasting — daily production ML
Modified XGBoost models forecasting the next 30 days of production for 8,000+ wells, inferring daily into corporate reporting — plus 2-year forecasts across the full well set. Read more
2022–2024
IROC analytics — remote operations centre
Real-time well status and production dashboards for Canlin’s Integrated Remote Operating Centre, with facility-outage impact auto-assessment built on networkx.
2023–now
Claude Code toolchain — skills · sub-agents · hooks · MCP
A cross-repo AI-native development practice: custom skills, sub-agents, hooks, and MCP servers — including profiling my own agent usage to find where the time was actually going, then promoting the dominant pattern into a first-class sub-agent.
2024–2025
Auto-Frac sequencer — optimization app
Streamlit app replacing a manual operations-scheduling task with a purpose-built optimization algorithm balancing efficiency, sequencing, and equipment-rotation constraints.
2024–2025
Modern-stack PoC — Python · dbt · cube.js · DuckDB
A fully-working local modern data stack (Python ingestion → dbt Core → cube.js → DuckDB → PowerBI, in Docker) that replaced legacy R + CSV flows and anchored the case for cloud analytics.
2025–now
US Dagster platform — 300M+ records · 945 assets
Production-grade Dagster-native platform for US oil & gas data: Polars/DuckDB performance layer, Terraform-managed AWS, schema contracts, and CI — the case study in shipping a modern data platform AI-first. Read more
What it adds up to
Data platform engineering
Dagster, dbt, Snowflake, Polars, DuckDB — three generations of platform builds, each one the foundation its company still runs on.
ML & AI engineering
LLM-in-the-loop pipelines, structured outputs, MCP servers and Claude Code toolchains on the AI side, and forecasting, classification and anomaly detection actually deployed on the ML side — 8,000 wells inferring daily into corporate reporting, not notebooks.
Analytics engineering & BI
Tableau and PowerBI as corporate platforms rather than one-off dashboards, semantic layers over the warehouse, and reporting automated end to end — the weekly production report alone gave Operations back 50+ hours a week.
Data leadership
Team coordination, mentorship, and board-level ownership — currently a 6-person cross-border team, with a decade of turning analysts into data professionals behind it.
Live preview of Twochannel's system builder — a flagship AI-native monorepo designed and built end-to-end.
Hiring for a data or AI team?
The fastest way to reach me is email — or the resume is one click away.