A shortlist typically within 72 hours, usually contributing inside two weeks, and 30-day replacement support if the fit is wrong (terms in your agreement).
Shortlist typically in 72 hours · 30-day replacement support · Month-to-month
Snowflake removes most of the infrastructure work from a data warehouse, which is exactly why teams adopt it quickly. The catch is that compute is billed by the second, and nothing stops a dashboard from waking a large warehouse every few minutes or a badly written query from scanning a whole table. Many companies discover their real Snowflake cost a quarter after go-live.
A senior Snowflake engineer designs models, warehouses and pipelines with cost in mind from the start, and can explain where every credit goes.
Clean, efficient SQL and models analysts can understand.
Warehouse sizing, auto-suspend, resource monitors and query tuning.
Snowpipe, streams and tasks, dbt, Airflow or Fivetran, with monitoring.
Role hierarchies, masking policies and least-privilege access.
Moving data and logic from legacy warehouses with tested parity.
Python transformations and ML preparation inside Snowflake when SQL is not enough.
Credit bills that keep climbing. Oversized warehouses, auto-suspend left at defaults, and full refreshes where incremental loads would do push costs up month after month.
Slow queries on big tables. Poor clustering and models that ignore how Snowflake prunes micro-partitions make analysts wait.
Loose access control. Without a role hierarchy and masking policies, sensitive data ends up visible to far more people than it should be.
Every engineer we put forward has at least five years of hands-on experience. Within that, here is how the levels differ.
Senior Engineer (5–8 years). Builds ELT pipelines with Snowpipe, streams and tasks or dbt, writes efficient SQL and models data for analytics. Comfortable with Python and Snowpark.
Lead / Architect (8+ years). Owns account design, role-based access, cost governance, data sharing, and migrations from Redshift, Teradata, SQL Server or on-premise warehouses.
Warehouse migrations. Moving from Redshift, Teradata, Oracle or SQL Server to Snowflake with tested data parity
dbt and modelling. Staging, intermediate and mart layers in dbt with tests and documentation
Cost reviews. Finding and fixing the warehouses, queries and refresh patterns that drive spend
Security and governance. Role hierarchies, masking and row access policies, and audit-ready access reviews
Week one. They review your account setup, warehouses, roles and the top queries by cost and runtime.
Weeks two and three. First pipelines or models delivered with tests, plus quick cost wins such as auto-suspend and warehouse sizing.
Week four. A written plan for modelling, governance and cost, with expected savings where they can be estimated.
One warehouse for everything. Separate warehouses for loading, transformation and BI make costs visible and controllable.
Ignoring query history. Snowflake shows exactly which queries cost the most. A good engineer looks there first.
Treating it like an on-premise database. Indexes, manual partitions and nightly full reloads are habits that don’t fit Snowflake.
Rates depend on seniority, scope and engagement length. See the cost guide for market ranges, or tell us the role and we will give you a firm number.
Tell us the role and we will typically send a shortlist within 72 hours.
Tell Us the RoleTell us the role. Three to five profiles, typically within 72 hours.