Data pipelines that don't break at 3am.

It depends on your existing cloud footprint, team skills, and workload mix. Snowflake is our default recommendation for mid-market companies starting from scratch — it separates compute from storage cleanly, the SQL interface is familiar, and the operational overhead is low. BigQuery fits well if your organization is already invested in Google Cloud. Databricks is the right choice when your workloads are compute-heavy, you need a unified platform for pipelines and ML, or you are processing semi-structured data at scale. The architecture design phase evaluates all three against your specific requirements before we commit to a platform.

Medallion architecture organizes your data warehouse into three layers: Bronze (raw data exactly as it arrives from source systems), Silver (cleaned, validated, joined data), and Gold (business-ready aggregates and metrics your analysts query directly). The layers create a clear separation between ingestion concerns and transformation concerns. When a source system changes schema, you update the Bronze-to-Silver transformation without touching every Gold model. When an analyst requests a new metric, you build a Gold model from trusted Silver tables without worrying about raw data quality. It is the single most effective structural choice for keeping a data warehouse maintainable as it grows.

Every dbt model in the transformation layer has a corresponding set of automated tests: schema assertions (columns exist with expected types), not-null checks on required fields, uniqueness checks on primary keys, and referential integrity checks between related tables. These run in CI on every pull request — a transformation change cannot be merged if it breaks existing tests. We also configure freshness assertions in Airflow that alert when a source fails to deliver data within its expected SLA window. The result is that your analysts know when data is late and why, rather than discovering stale numbers in a dashboard on Monday morning.

Yes. Airbyte and Fivetran together cover most SaaS sources (Salesforce, HubSpot, Stripe, NetSuite, and hundreds more) through maintained connectors. For custom databases (PostgreSQL, MySQL, SQL Server, MongoDB) we configure CDC (change data capture) or scheduled extracts depending on your latency requirements. For proprietary APIs or internal systems without a connector, we build custom Airbyte connectors or Python-based extractors. The architecture phase maps every source system to an extraction strategy before any pipelines are built.

Compliance-ready architecture means the structural choices you make today do not become liabilities during an audit later. Concretely: PII fields are identified at the Bronze layer and masked at Silver using column-level security, so analysts never query raw PII in production. Data lineage is documented automatically through dbt's generated docs, giving auditors a traceable path from source to dashboard. Access is role-based and logged — every query to sensitive tables is auditable. We do not offer certification services, but the pipelines we build are designed to support your SOC 2, HIPAA, or CCPA audit without requiring a separate remediation engagement.

That is the design goal. The dbt codebase follows consistent naming conventions, documented patterns, and modular model dependencies so your team can add a new source by following the same Bronze-to-Silver-to-Gold pattern we establish together. The Airflow DAG structure uses a templated pattern for new sources. We run structured onboarding sessions covering the full development workflow: adding a connector, writing a dbt model, writing tests, opening a PR, and promoting to production. Handoff is not a slide deck — it is paired working sessions until your team is confident.