Matthew Nelson

Product ownership and team leadership

• Run roadmap, prioritization, and code review for a 6-person team across US and Canadian data codebases — while still shipping in the Dagster platform weekly.
• Built PM tooling as software, in daily use: a Jira roadmap auto-scheduler, human-gated production-failure triage, and skills for weekly updates and Jira templating.
• Set Stack Maps product direction (frontend + API), scoping and prioritizing with the frontend and API engineers against the data-platform realities underneath.

Modern Data Platform Architecture

• Architected a production Dagster platform across 15 US states and 945 assets, distilling a 3.8 TB source layer into a 10.3 GB exposure layer serving 300M+ records.
• Converted the Texas production fact build — the largest on the platform — to a streaming Polars plan, eliminating a repeat production out-of-memory failure.
• 146 contract-driven freshness checks across all 15 states, fanning out to 468 evaluations, threshold-calibrated to kill false-positive alerts.
• Designed reusable exposure/coalesce patterns so legacy mart schemas ride on the new fact-table architecture and new states extend the pattern instead of needing bespoke builds.

AI-native Developer Workflow

• Built a project-specific Claude Code toolchain — a parquet-inspector sub-agent, an auto-format hook, and task skills for pipeline triage and schema work — over a token-optimization layer (618M tokens saved to date).
• Profiled my own Claude Code conversation history to find the highest-frequency ad-hoc work, then promoted the dominant pattern into a first-class sub-agent.

Cloud Infrastructure & DevOps

• Deployed the AWS footprint with Terraform IaC across staging and production — RDS, S3, and ECS compute launched and managed per-job by Dagster.
• Implemented CI/CD (GitHub Actions, ruff, pyright, asset schema-contract tests) plus the branching and deployment automation behind staging → prod releases.

Framework & Team Enablement

• Implemented 10 of 15 state pipelines solo, then onboarded two non-platform engineers who shipped the rest by following the documented conventions and schema contracts.

AI-assisted product catalog at scale

• Brand-scraper → LLM normalizer → reviewed dedup pipeline now maintains 18k+ products across 2k+ brands in Directus with minimal manual intervention.
• A 12-agent Claude Code curation team with human approval gates runs catalog curation — three pipeline defects caught at the gates, zero silent corruptions of production state.
• Wizard captures budget, room and genres; an async build job then returns complete system variants, constrained by signal-chain adjacency and per-category budget allocation.
• Replaced a three-tier dedup cascade and a fixed 0.9 confidence cutoff with one Claude decision service holding customer catalog writes to the curation team's standard.

Platform Architecture

• Turborepo monorepo shipping Next.js (Vercel), Directus (Railway), Algolia search, Neo4j relationships, and Doppler-managed secrets across three hosting targets.
• Shipped GDPR Article 17 erasure — a twelve-step deletion pipeline with audit trail, CI-enforced FK rules, and weekly orphan sweep — backed by a 65-test Playwright E2E suite green on prod.

Dashboards & Corporate Reporting

• Built the corporate PowerBI dashboard suite (Netback, Operating Cost, Capital) — corporate-to-well drill-downs used by executives, operations, production engineering, and development.
• Automated the weekly well production report end-to-end (Spotfire + Power Automate), removing a half-day weekly manual process from the analytics manager's plate.

Custom Web Applications

• Replaced a manual 2–3-hour Excel frac-scheduling process with a Streamlit optimization app — its sequence plans ran ~8 multi-well pads.

Data Governance & Platform Advocacy

• Local modern data stack PoC (Python / dbt / cube.js / DuckDB / PowerBI in Docker) replacing legacy R + CSV processes.
• Data-governance-committee advocate for cloud-first analytics; educated senior leadership on modern stack design.

Corporate Data Warehouse on dbt Cloud + Snowflake

• Built the dbt Cloud / Snowflake platform that became Canlin's foundation for operational data, BI, and ML — SCADA to Accounting in a single source of truth.
• Automated the weekly production reporting process end-to-end — backend workflows, input sheets, and the report itself — saving 50+ hours/week across Operations.

Data Science & ML Ops

• Deployed a modified XGBoost forecaster inferring daily across 8,000+ wells (30-day horizon), plus 2-year forecasts of the full well set, feeding corporate reporting.
• Built the real-time well-status analytics behind Canlin's remote operations centre (~500 wells), with networkx-based outage impact auto-assessment and anomaly-detection reporting.
• Streamlit SME-labeling app + classification model ranking well risk across the portfolio, with explanations.
• Home-grown ML-ops layer (Streamlit + Snowflake + NocoDB) for labeling and data input, with DataRobot for productionized models.

Tableau data-warehouse implementation

• Implemented Tableau as the corporate reporting platform — 100+ data sources, 50 workbooks, and 20 Prep flows published in year one.
• Mentored every Tableau Creator/Explorer in the company and made the executive case for self-service data.

Corporate Reserves — regulated annual asset valuation, 5 years

• Owned the externally-audited NI 51-101 reserves evaluation for 5 years — technical revisions added +$730M (NPV10) across four consecutive cycles.

Corporate Acquisitions & Divestitures

• Built the data flows and dashboards for rapid A&D evaluation through a depressed-price divestiture cycle that brought corporate debt from $120M to $0

Asset-longevity analysis & internal tooling

• Multi-scenario asset-longevity modeling across all operated facilities that set 5–10-year corporate strategic direction
• Built a VBA-driven opportunity-tracking tool with a staging/approval workflow for development planning

Highlights across both roles

• On-site production/operations engineering, including long-term sensor-data trending and equipment monitoring
• Multi-dimensional tracking models built from time-series sensor (thermocouple) data
• Drill-program design and feasibility analysis across multiple assets