Snowflake data cloud consultants and data engineers

Digiventi provides Snowflake solutions architects who assess existing data platforms and define an implementation plan around reporting requirements. They review account structure and workload separation, with cloud-region choices informed by residency requirements.

For migrations from SQL Server or Teradata, architects map dependencies and identify SQL conversion needs before planning staged cutovers. Snowflake consultants can also review an existing implementation, documenting configuration gaps and prioritizing changes before additional workloads move onto the platform. Migration validation covers record reconciliation and business-report comparisons.

Snowflake data engineers replace manual data preparation with monitored pipelines and maintainable models. They connect source systems to the analytics layer and investigate discrepancies between operational records and reports.

• Ingestion pipelines: Configure batch loads or Snowpipe ingestion, with handling for schema changes and failed records.
• Analytics models: Develop SQL and dbt transformations with automated tests. Use Snowpark Python where processing requirements extend beyond SQL.

Engineers can connect curated datasets to Power BI or Tableau and reconcile metric definitions with reporting owners.

Snowflake consultants configure role-based access control and review privileges when data access has grown without clear ownership. They implement masking policies and row access policies against documented requirements, then validate behavior with representative user roles.

For cross-department or external collaboration, specialists configure Secure Data Sharing and check which datasets recipients can access. Access reviews and audit queries provide evidence for internal controls. Retention settings and regional data placement can be assessed against your GDPR obligations with your compliance owners.

Snowflake performance specialists use Query Profile and ACCOUNT_USAGE views to investigate slow queries or unexpected credit consumption. They adjust warehouse sizing and auto-suspend settings, then test SQL changes against representative workloads. Clustering is assessed where query patterns justify its maintenance cost.

For ongoing support, data engineers investigate failed tasks and delayed loads, restore affected datasets where retention permits, and document recovery steps. Resource monitors and usage reporting make spending attributable to workloads, while regression checks help identify performance changes after releases.