Hire senior Snowflake engineers in your time zone.

A senior Snowflake engineer owns the reliability, performance, and cost of the warehouse that your analytics, reporting, and often your product decisions run on.

Core responsibilities include:

• Data modeling. Designing schemas, star models, and data marts that hold up as data volume and query complexity grow, including handling semi-structured data like JSON alongside structured tables.
• Pipeline ownership. Building and maintaining the ELT pipelines, often through dbt, Fivetran, or custom code, that load and transform data from source systems into Snowflake.
• Cost and performance management. Right-sizing virtual warehouses, setting up auto-suspend and auto-scale policies, and tuning queries so compute cost does not creep up silently as usage grows.
• Governance and access. Setting up role-based access control, masking policies for sensitive data, and audit trails that satisfy compliance requirements.
• Cross-team enablement. Making sure analytics, data science, and BI teams can self-serve against clean, documented data without needing a data engineer in the loop for every question.

The senior-level judgment shows up in how they balance these against each other: knowing when a slow dashboard is a modeling problem versus a warehouse-sizing problem, and when to invest in a proper transformation layer instead of letting ad hoc SQL scripts accumulate across the organization.

Snowflake rarely operates alone. It sits at the center of a modern data stack, and the strongest hires are fluent across the tools that feed data in and pull insight out.

Look for experience with:

• Transformation and orchestration. dbt for building and testing transformation logic as code, and Apache Airflow for scheduling and orchestrating multi-step pipelines.
• Ingestion tooling. Fivetran or similar connectors for pulling data from operational systems, SaaS tools, and event streams into Snowflake.
• Cloud platforms. AWS, Azure, or Google Cloud, since Snowflake runs on top of one of these and integrates closely with each provider's storage and identity systems.
• Complementary processing engines. Python and Apache Spark for workloads that go beyond SQL, including Snowpark for running Python data engineering directly against Snowflake.
• BI and analytics tools. Tableau, Looker, or Power BI, since the warehouse's real value shows up in the dashboards and reports built on top of it.

A candidate who only knows Snowflake's SQL surface but cannot speak to how data gets in, how it gets transformed, and how it gets consumed downstream is missing most of the job.

Snowflake's SQL interface is approachable enough that many candidates can write a basic query on day one. The gap between that and a senior hire shows up in how they think about modeling, cost, and governance at scale.

Ways to evaluate a candidate:

• Ask them to walk through a schema or data mart they designed, and why they made specific modeling choices for that use case.
• Ask how they approach warehouse sizing and cost control, since this is one of the clearest signals of real production experience versus tutorial-level familiarity.
• Check their experience with dbt or another transformation layer, and how they test transformation logic before it reaches production dashboards.
• Probe their approach to access control and data governance, especially for sensitive data that needs masking or row-level security.
• Ask about a slow or expensive query they diagnosed and fixed, and what they changed to bring the cost down.

BetterEngineer already runs this kind of evaluation, including technical screens with practicing data engineers, before you ever see a profile, so the candidates you receive have already demonstrated this depth.

Quick evaluation checklist:

• Ask for examples of schemas or data marts they have designed in Snowflake
• Review how they approach warehouse sizing and cost control
• Check experience with dbt or another transformation layer
• Probe their approach to access control and data governance
• Ask how they diagnosed and fixed a slow or expensive query