Cloud Architecture & Data Foundation

For a governed Snowflake data foundation, we put first value in production in 8–16 weeks. The mechanism matters more than the number: a discovery fixes scope up front, delivery runs in use-case sprints, and each slice proves out before it scales. A full estate takes longer, but no one waits until the end to see working data products.

A structured audit of an existing Snowflake environment: account topology, security and access design, cost drivers, and pipeline reliability, measured against how Snowflake is built to run. You get a prioritized findings list with the reasoning documented, so your team can act on it with us or on its own.

Iceberg makes sense when other engines need to read the same tables, when data volumes push toward open storage economics, or when open formats are organizational policy. Open Catalog (Polaris) keeps those tables governed either way. When workloads live end to end in Snowflake, native tables are often simpler; discovery is where we decide with evidence rather than defaults.

Data governance on Snowflake is enforced as configuration on the data itself, not as a policy document. Role-based access is modeled to the organization so people see only the data they need, enforced in Snowflake rather than bolted on afterward, and sensitive data is classified and masked with tagging plus row and column policies from the first table built. Horizon Catalog carries lineage and access history, so every figure is traceable to its source and every access is logged for audit. All of it runs in the client's own Snowflake account, so no copies leave that perimeter.

A golden record is built in layers inside the client's own Snowflake account. Source data lands in Bronze, is cleaned and conformed in Silver, and resolves into governed Gold records, with master keys, matching and merge, survivorship rules, lineage, and audit designed in from the first table. All the modeling runs in dbt under Git, so every rule that decides which value survives is reviewed, tested, and traceable. For a commercial-vehicle dealer group, Openflow lands the ERP, dealer management system, and payroll and HR system on a nightly incremental schedule, producing one governed version of customer, vehicle, part, supplier, and employee, each traceable back to its source.

Master data domains are mastered in sequence, so each one reaches a trusted golden record in turn rather than all at once. In the master data build for a commercial-vehicle dealer group, eight business domains, from aftersales and parts catalogs to finance and HR, are delivered as three releases across a twelve-month roadmap. dbt tests validate matching, survivorship, and conformance on every run, so a bad record is caught before it reaches Gold and each domain stays trustworthy after the release that created it.