Snowflake Data Engineer

CloudHive; San Francisco (CA); Hybrid; USD 140,000 - 180,000; Full time

Job summary

CloudHive is seeking a Snowflake Data Engineer in San Francisco, a hybrid role combining on-site collaboration with remote work. You will design and optimize data pipelines, create robust data models, and support AI-ready data foundations across the organization.

You will work closely with data scientists, growth analytics, and stakeholders to ensure data is clean, accessible, and ready for analytics and AI initiatives. This role emphasizes scalability and data reliability.

Qualifications

• Strong experience with Snowflake data engineering and data warehousing.
• Advanced SQL for complex transformations and queries.
• Python development for data pipelines and automation.
• Hands-on dbt for transformation and modeling.

Responsibilities

• Design, build, and maintain production-grade data pipelines with scalable, reliable data flows.
• Leverage Snowflake to optimize queries and data storage, and build robust data models.
• Develop reusable data models using dbt for analytics and AI use cases.
• Implement automated data quality checks, validation, and monitoring.

Skills

Snowflake data engineering

Advanced SQL

Python

dbt

Git/GitHub

Data modeling

ETL/ELT

Cross-functional teamwork

Job description

Location: San Francisco (Hybrid - 3 days onsite)

About the Role

We're seeking a skilled Snowflake Data Engineer to support our team with critical data engineering and data strategy initiatives. In this role, you'll develop and enhance reliable data pipelines, data modeling frameworks, and AI-ready data foundations that power analytics, reporting, and AI applications across the organization. Your work will directly improve the accessibility, reliability, and usability of data for stakeholders and downstream systems.

Key Responsibilities

• Design, build, and maintain production-ready data pipelines that efficiently ingest, transform, and deliver data from multiple source systems. Focus on scalability, reliability, and performance to support growing data volumes and use cases.

Snowflake Data Engineering

• Leverage Snowflake's capabilities to implement robust data architectures, optimize query performance, and manage data storage strategies. Build efficient data models that balance analytical flexibility with performance.

Data Modeling & Transformation

• Develop reusable data models using dbt that serve multiple analytics and AI use cases. Implement dimensional modeling, data normalization/denormalization, and transformation logic that maintains data integrity throughout the pipeline.

Data Quality & Validation

• Establish and enforce data quality standards through automated validation, testing, and monitoring. Proactively identify and resolve data quality issues to ensure downstream consumers have confidence in the data.

Source System Integration

• Integrate data from diverse source systems, managing complexities around data formats, schemas, incremental loading patterns, and change data capture requirements.

Documentation & Metadata

• Create comprehensive documentation and metadata for datasets, pipelines, and data models. Ensure data lineage is clear and data assets are discoverable and understandable by technical and non-technical stakeholders.

AI-Ready Data Foundations

• Support AI initiatives by ensuring data is properly structured, cleaned, and accessible for machine learning and AI applications. Collaborate with data scientists and AI engineers to understand requirements and optimize data for their use cases.
• Work closely with Growth analytics teams, data scientists, and business stakeholders to understand requirements and provide engineering support for analytics and AI use cases.

Required Skills & Experience

• Strong experience with Snowflake data engineering, including data warehousing, SQL optimization, and cloud data architecture
• Advanced SQL proficiency for complex data transformations and analysis
• Python development experience for data engineering workflows and automation
• Hands-on experience with dbt for data transformation and modeling
• Proficiency with Git/GitHub for version control and collaborative development
• Strong understanding of data modeling principles, including dimensional modeling and normalization techniques
• Experience with source system integration and ETL/ELT patterns
• Self-starter who can work independently while collaborating effectively with cross-functional teams