Data warehouses, ETL/ELT pipelines, BI dashboards, reverse ETL, governance. We build the plumbing that moves data where it needs to be, and the surface where decisions actually happen. Lineage documented, quality monitored, access scoped.
Data work is only valuable when the numbers are trusted, the dashboards are read, and the plumbing stays running without constant intervention. Our deployments prioritize reproducibility, lineage, and tools your team can actually operate.
Snowflake, BigQuery, Redshift, or Postgres-based warehouses sized to your actual data volume. Right-sized compute, cost monitoring, retention policies. No "we need Snowflake because everyone has it" recommendations.
Managed connectors (Fivetran, Airbyte) for standard sources. Custom Python/dbt for the long tail. Scheduled orchestration via Airflow or Dagster. Everything versioned in your git repo, not scattered across GUI tools.
Metabase or Looker for the main dashboarding layer. Power BI or Tableau when your org already runs on them. Dashboards designed for the people who use them, not the people who build them.
Warehouse-computed segments pushed back into the tools where work happens: Salesforce, HubSpot, Zendesk, Braze. Data stops being locked in dashboards nobody opens and starts driving automation.
Lineage from source to dashboard, dbt tests on critical columns, freshness SLAs with alerting, role-based access control. Audit-ready evidence that your numbers are the right numbers.
Not a jumble of SaaS tools stitched together with CSV exports. Warehouse, transformation layer, and consumption surface designed as one stack, documented together, operated by the same people.
Warehouse choice driven by your volume and cost model, not vendor sales pitches. Snowflake for seasonal workloads, BigQuery for Google-adjacent orgs, Postgres-based (Supabase, RDS, self-hosted) for smaller data volumes where simplicity wins. Ingestion via Fivetran/Airbyte for standard sources, custom code for the long tail.
Platforms we deploydbt as the default transformation layer. Models versioned in git, tested with column assertions, documented automatically, scheduled through Airflow or Dagster. When something breaks you get a specific test failure in a slack channel, not a vague "the dashboard looks wrong" email on Monday morning.
Platforms we deployMetabase or Looker for self-serve BI, Power BI or Tableau when your org already runs on them. Dashboards designed for the audience that uses them. Reverse ETL (Hightouch, Census) pushes warehouse-computed segments into Salesforce, HubSpot, and Zendesk so data stops being locked in a dashboard nobody opens.
Platforms we deployMost data engagements end with a tool nobody can modify, metrics nobody can trace, and a Snowflake bill nobody can explain. Here is how we work differently.
Every metric traceable from dashboard to source column. dbt's built-in lineage graph plus documented data contracts means when finance asks "what does revenue include?" the answer is in the model, not in someone's head.
When regulators ask the same question later, the answer is still there.
dbt, Airflow, Metabase, Airbyte, Dagster: all open-source cores, deployable to your own cloud or self-hosted. Managed cloud versions when they genuinely save your team time, but the underlying tools stay portable.
You are never one vendor pricing change away from a migration.
Snowflake credits attributed to warehouses and roles. BigQuery slot usage per query. Monthly spend review showing which queries, dashboards, or users are driving cost, with recommendations to tune.
When the warehouse bill spikes 40%, we tell you which model caused it and how to fix it.
For many Canadian SMBs, Postgres is enough. If your data fits comfortably in a modestly-sized Postgres instance (hundreds of GB, not TB) and your query patterns are known, Postgres with proper indexing and materialized views runs circles around a $5k/month Snowflake bill.
Snowflake, BigQuery, and similar MPP warehouses start making real sense when: your data genuinely crosses the single-node ceiling, you have unpredictable analytical workloads that need auto-scaling, or your team benefits from separation of storage and compute. We assess which bracket you are actually in.
Three things dbt gives you that raw SQL does not: dependency management (models rebuild in the right order), testing (assertions on columns, failing the pipeline when data is wrong), and documentation (auto-generated model docs with lineage graphs).
The alternative is SQL files in a shared folder with implicit dependencies, no tests, and documentation that lives in someone's head. dbt turns data transformation into something that behaves like software: versioned, testable, reviewable in pull requests.
Yes, and that is a common engagement starting point. Typical first-pass audit finds 20-40% savings from: queries that scan full tables when a partition would do, dashboards refreshing hourly that nobody looks at, dev warehouses running 24/7, and oversized compute for workloads that run twice a day.
Ongoing: monthly cost review attributing spend to specific models, users, and dashboards. When something spikes we identify the cause within a day, not after the quarterly bill arrives.
Governance built in: role-based access control at the warehouse level (data engineers get write, analysts get read on marts, business users get read on views only), PII handling via column-level masking or vaulting, row-level security where sensitive data mixes.
For regulated workloads (PIPEDA, Quebec Law 25, OSFI B-13), we align with the compliance team on data residency, retention, and audit logging requirements. Lineage from dbt plus access logs from the warehouse give audit teams the evidence they need.
Yes, that is often the best engagement model. Three common patterns:
Pattern 1, Build and hand over. We build the stack, document it, train your analysts, then step back. Your team runs it.
Pattern 2, Augment. Your team runs the day-to-day (dashboards, ad-hoc analysis), we handle the heavier platform work (pipeline design, cost tuning, governance).
Pattern 3, Fractional data engineering. You have analysts but no data engineer; we fill that role on retainer so you do not have to hire for it.
Everything is yours by design. dbt models in your git repo, Airflow DAGs in your git repo, Metabase self-hosted on your infrastructure (or using your own Metabase Cloud account), warehouse under your own cloud account.
If you move the operation in-house or to another provider, the person taking over inherits a documented, versioned, portable stack. The 30-day handover transfers access, documentation, and operational knowledge. No lock-in, no "we hold your credentials" nonsense.