Snowflake Architect - Data Build Tool

Description :

We are seeking a visionary Solution Architect to design, lead, and evolve our modern data platform. You will be the primary architect responsible for leveraging Snowflake and dbt to build a scalable, high-performance data ecosystem. This role is at the intersection of architecture, analytics engineering, and data science, requiring a leader who can translate complex business goals into robust technical frameworks while ensuring operational excellence across the entire data lifecycle.

Roles and Responsibilities :

1. Strategic Data Architecture :

- Platform Design : Architect end-to-end data solutions on Snowflake, focusing on multi-cluster warehousing, advanced security configurations (RBAC), and cost-efficient scaling.

- ELT Strategy : Lead the transition to modern ELT patterns, ensuring data flows efficiently from source to consumption layers.

- Data Modeling : Define the core data architecture, utilizing industry-standard modeling techniques (Star Schema, Snowflake Schema) to support diverse analytics needs.

2. Analytics Engineering & Governance :

- dbt Leadership : Own the dbt environment, defining standards for models, macros, snapshots, and documentation to ensure a "code-first" approach to data transformation.

- Quality Frameworks : Implement automated data quality testing and observability frameworks within dbt to ensure trust in downstream reporting.

- DevOps for Data : Oversee CI/CD pipelines for the data stack, ensuring seamless deployments, version control (Git), and rigorous code review processes.

3. Machine Learning & Advanced Analytics :

- ML Integration : Partner with Data Scientists to design and deploy ML solutions that leverage Snowpark and Snowpark ML, bringing compute directly to the data.

- Feature Engineering : Architect scalable feature stores and data pipelines specifically optimized for ML model training and inference within Snowflake.

4. Collaboration & Mentorship :

- Stakeholder Alignment : Act as the technical liaison between data engineers, business analysts, and executive stakeholders to ensure the platform meets long-term business objectives.

- Best Practices : Establish and evangelize best practices for SQL development, performance tuning, and documentation across the data organization.

Technical Requirements :

Must-Have Skills :

- Snowflake Mastery : Extensive hands-on experience with Snowflake architecture, including performance tuning (clustering, search optimization), security (masking policies, row-level security), and cost governance.

- Dbt Proficiency : Advanced experience with dbt (Core or Cloud), including complex macros, materialization strategies, and test suites.

- Cloud Architecture : Proven track record of designing data solutions in major cloud environments (AWS, Azure, or GCP).

- SQL & Modeling : Expert-level SQL skills and a deep understanding of ELT/ETL best practices.

Nice-to-Have (Preferred) :

- Data Vault 2.0 : Experience implementing Data Vault 2.0 methodologies for agile, scalable data warehousing.

- Snowpark : Hands-on experience with Snowpark (Python/Java/Scala) and external functions for advanced processing.

- Infrastructure as Code (IaC) : Familiarity with tools like Terraform for managing data infrastructure.

Success Indicators :

- Efficiency : Significant reduction in data processing latency and cloud compute costs.

- Reliability : High uptime of data pipelines and a robust suite of passing data quality tests.

- Adoption : Successful deployment of ML models into production using Snowflake-native capabilities.