Services
Data engineering and analytics
Most organisations do not have a data problem so much as a plumbing problem: the numbers exist, but they sit in systems that do not talk, in definitions no two teams agree on, and in reports assembled by hand each month. Corpshore Canada builds data pipelines, warehousing and business intelligence from Canadian pods in your time zone, at 35 to 55 percent of the cost of equivalent in-house Canadian hiring, turning scattered operational data into governed and reportable insight. We treat data governance and privacy as design inputs rather than a later phase, because a pipeline built without them has to be rebuilt. Data residency is configured to your requirement including Canadian-only hosting where a public sector buyer asks, and cross-border transfer positions are documented wherever data moves. The measure of the work is a decision made from trusted numbers, not a dashboard no one believes.
How the service works
- 1
Discovery and data modelling
We map the sources, the definitions and the questions the business actually needs answered, not the ones a generic dashboard offers. That means reconciling where two systems disagree on the same metric and agreeing a single definition before anything is built. The output is a data model and a governed set of definitions, so the warehouse answers questions consistently rather than plausibly.
- 2
Pipelines and warehouse
We build the ingestion and transformation pipelines as code, with tests and monitoring, into a warehouse designed for the questions rather than a copy of every source table. Data quality checks run in the pipeline so a bad load is caught before it reaches a report. Privacy and residency are enforced in the design rather than bolted on, and lineage is tracked so every figure can be traced to its source.
- 3
Analytics and reporting
We build the business intelligence layer on the governed model, so a metric means the same thing wherever it appears and a number can be traced back to the row that produced it. Reports and self-service are designed for the people who use them rather than the team that builds them. Where a definition is contested we surface it rather than picking one silently.
- 4
Operate and extend
In steady state the pod runs the pipelines, monitors data quality and extends the model as new questions arrive, retiring the manual spreadsheets the platform replaces. Data quality is reported as a first-class metric rather than assumed. Governance runs on an agreed cadence so a broken feed or a drifting definition is caught and owned rather than discovered in a board pack.
How we deliver it from Canada
Delivery is from Canadian pods in Ontario, Quebec and Alberta, in English and Canadian French, with full working-hours overlap across Eastern, Central and Mountain time. We build in your cloud and your data platform rather than moving your data into ours, so residency, access and audit stay under your control. Data residency is configured to your requirement including Canadian-only hosting where a public sector buyer asks.
Squad, 3 to 5 specialists
A data engineering pod under a lead with analytics engineering built in, suited to a defined pipeline or warehouse build with governance from the start.
Data team, 6 to 12
Data engineers, analytics engineers and a business intelligence developer under a delivery lead, with data quality and governance reported against agreed thresholds.
Practice, 12 and above
A data practice with an engineering manager, a governed shared model across domains and a blended Canadian and distributed model where scarce skills require it.
Compliance and data handling
Data engineering complies with the Personal Information Protection and Electronic Documents Act by default, with Quebec Law 25 requirements applied where personal information of Quebec residents is processed, including de-identification and purpose limitation. Controls follow the relevant group framework, with SOC 2 or ISO 27001 readiness supported where the business requires it and certification scope confirmed on request. Data residency, minimisation and cross-border transfer positions are documented explicitly wherever data is stored or moved outside Canada.
Technology
We work in your data platform rather than prescribing one, across common cloud data warehouses, ingestion and transformation frameworks, orchestration and business intelligence tooling. Data quality, lineage and access controls are standard parts of the build rather than optional extras. Specific platform depth is confirmed against your environment during discovery, and where a capability is missing we integrate proven tooling under your governance instead of a dependency you cannot maintain.
How performance is measured
- Pipeline reliability and freshness against agreed schedules
- Data quality pass rate on validated datasets
- Definition coverage in the governed metric layer
- Time from question to trusted answer
- Manual reporting hours removed
Reporting treats data quality as a first-class metric, on an agreed cadence, alongside pipeline reliability and the progress of the governed model. Where two sources disagree on a number we bring the discrepancy and a recommended definition rather than publishing a figure that will be argued with later. The intent is that the reporting the platform produces is trusted enough that decisions stop waiting for a manual reconciliation.
Where this applies
Banking and financial services
Governed data pipelines and regulatory reporting where lineage and a single agreed definition matter as much as the number, with sensitive data kept in Canada.
Insurance
Claims, underwriting and operational analytics on a governed model, so pricing and reserving decisions rest on figures that reconcile rather than compete.
Energy and natural resources
Operational and sensor data pipelines for distributed assets across Alberta and western Canada, turning scattered field data into reportable operational insight.
Pricing and engagement models
Data work is priced as a project for a defined pipeline or warehouse build, a managed data service that runs and extends the platform, or staff augmentation into your existing data team. A project suits a bounded build; a managed service suits a platform that has to run and stay trustworthy; augmentation suits a specific skills gap. We build for a model you can operate rather than a dependency on us.
Frequently asked questions
Why do our reports never seem to agree?
Usually because the same metric is defined differently in each system and reconciled by hand every month, so the numbers diverge quietly. We fix the cause by agreeing a single governed definition and building the warehouse on it, so a figure means the same thing wherever it appears and can be traced back to the row that produced it rather than defended in a meeting.
How do you keep the data trustworthy over time?
Data quality checks run inside the pipelines rather than as an afterthought, so a bad load is caught before it reaches a report. We monitor freshness and quality, treat data quality as a reported metric and track lineage so every figure can be traced to its source. A silent data quality failure is treated as an incident, not a rounding error.
Where does our data live and who can see it?
We build in your cloud and your data platform rather than moving your data into ours, so residency and access stay under your control. Data residency is configured to your requirement including Canadian-only where a public sector buyer requires it, personal data is minimised and de-identified where possible, and cross-border positions are documented wherever data moves.
Can you work with the tools we already have?
Yes. We work in your existing data warehouse, ingestion and business intelligence tooling rather than steering you to a platform we prefer. Where a genuine capability gap exists we recommend and integrate proven tooling under your governance, and we confirm specific platform depth against your environment during discovery rather than claiming it generically.
Do we need a big bang project or can we start small?
You can start small, and we usually recommend it. We build the governed model and the first high-value pipeline, prove the pattern and the trust it creates, then extend domain by domain. That sequences value early and avoids the multi-year data platform programme that consumes budget long before it produces a decision anyone acts on.
How does this support analytics or AI later?
A governed, well-modelled warehouse with tracked lineage is the foundation both analytics and AI depend on, because a model trained on ungoverned data inherits its inconsistencies. We build the data layer so that advanced analytics and AI have trustworthy, documented inputs, and the AI delivery practice can build on the same governed foundation when you are ready.
Build your Canadian team
Tell us the work, the languages and the coverage you need. You will have a considered response within six hours, or book a discovery call now.
Looking for a role rather than a partner? Explore careers at Corpshore Canada