Analytics Engineering

An analytics engineer transforms raw data into clean, tested, documented models that the rest of the business can trust and query directly, sitting between data engineering (getting data in) and data analysis (using the data). In practice, this usually means building and maintaining dbt models on top of a warehouse like BigQuery or Snowflake.

dbt (data build tool) is the industry-standard framework for transforming data inside a warehouse using SQL, with built-in testing, documentation, and version control. Companies use it because it turns ad-hoc, undocumented SQL scripts into a maintainable, auditable system that multiple people can safely work on at once.

No, dbt scales down as well as it scales up, and even a small team benefits from having tested, version-controlled data models instead of one-off queries no one remembers the logic behind. The complexity of dbt itself doesn't grow with company size; what grows is the number of models you need.

BigQuery is typically the better fit if you're already in the Google Cloud ecosystem and want serverless, pay-per-query pricing with less infrastructure management; Snowflake is often preferred for multi-cloud flexibility, more mature governance features, and workloads with heavy concurrent usage across teams. Neither is universally "better". The right choice depends on your existing stack, team skills, and query patterns, which is usually worth a short technical assessment before committing.

We build custom dbt models specific to your business logic and metrics definitions, because templated models rarely match how a specific business actually defines revenue, churn, or other core metrics. Generic templates can be a useful starting scaffold, but the modeling layer needs to reflect your actual business rules to be trustworthy.

Existing SQL logic is typically audited and rebuilt as tested dbt models rather than copied over as-is, since most legacy SQL scripts have accumulated undocumented assumptions and edge cases over time. Reports connected to the old logic are then repointed to the new, tested models once validated side-by-side.

We run new models in parallel with existing reports and reconcile the numbers line-by-line before cutting over, so any discrepancy is caught and explained before stakeholders ever see a different number. This validation step is a standard part of any migration, not an optional add-on.

Yes, we build pipelines from common SaaS tools (Salesforce, HubSpot, Stripe, and others) as well as custom product/application databases into your central warehouse. Most SaaS integrations use existing connectors (like Fivetran or Airbyte); custom or unusual sources are built directly.