About

Silverarc is a data engineering consultancy based in Toronto, Canada. We help companies design and build data infrastructure that is reliable, observable, and built to last — whether you're starting from scratch or untangling years of technical debt.

We work across the full data stack: infrastructure, pipelines, warehouses, orchestration, and the tooling that keeps everything running in production. Our engagements are focused and outcome-driven, with a bias toward clean architecture and pragmatic choices over complexity for its own sake.

Work

The goal in these engagements was always the same: give operations teams the clean, reliable data they need to make decisions and move the business forward. We got there by modernizing the underlying platform — rebuilding fragile, over-engineered ingestion pipelines in Dagster; reshaping dbt projects by tightening naming conventions, expanding test coverage, and stripping unnecessary complexity; consolidating scattered workflows into a single orchestration layer; and introducing Claude-powered automation for routine analytical tasks. We've also advised on larger migrations, including moves away from legacy data platforms toward dbt and BigQuery.

For a luxury watch retailer, the value was in making possible what hadn't been before. The peer-to-peer watch market runs on a single platform, but it wasn't built for operators — raw listings, no analytics, no way to see the market clearly. We built a market listing aggregator from scratch to change that: a full-stack system combining real-time Kafka pipelines, LLM-powered text parsing to turn unstructured listing data into clean structured records. The result was a product that directly informed buying and selling decisions and translated into revenue.

With a consulting firm making its first foray into data analytics and engineering, the priority was getting the foundation right. We work closely with their implementation team to accelerate their growth, prevent costly early mistakes, and help them build the kind of internal capability that compounds over time. This includes AI enablement: upskilling their engineers in AI tooling so they can continue to deliver their best work and maximize their leverage as the space evolves.