Snowflake Developer

About the Opportunity

Job Summary

The Snowflake Developer is responsible for designing, developing, optimizing, and operationalizing Northeastern University's Snowflake data platform. This role requires deep expertise in Snowflake's core capabilities-including data modeling, performance tuning, ELT pipelines, security controls, and orchestration-while also preparing the university for advanced AI and data product capabilities using Snowflake Cortex, Snowpark, vectorized storage, and machine-learning features.

The developer will work across legacy data structures (Banner, Workday, Salesforce, ServiceNow, and other enterprise systems) and build modern, scalable data pipelines and transformations that enable analytics, automation, applied AI, and agentic workflows. This role is foundational to transforming our data platform into an AI-ready environment.

*Applicants must be authorized to work in the United States. The University is unable to work sponsor for this role, now or in the future.

Minimum Qualifications

Education: Bachelor's degree in Information Systems, Computer Science, Engineering, or a related field required

Experience:

• 4-6+ years of experience building cloud-native data pipelines or enterprise data platforms.
• 3-5+ years of direct Snowflake development experience.
• Demonstrated ability to modernize legacy data structures and migrate workloads into Snowflake.
• Experience working with enterprise systems such as Workday, Salesforce, and/or ServiceNow.

Technical Competencies

• Deep knowledge of Snowflake architecture, warehouses, micro-partitioning, clustering, performance tuning, and cost optimization.
• Strong SQL and ELT development skills including DBT, Fivetran, Informatica, or similar tools.
• Experience with Snowflake Cortex, Snowpark, UDFs, dynamic tables, streams, and tasks.
• Familiarity with AI workloads: embeddings, vector search, RAG, LLM orchestration pipelines.
• Experience with ETL/ELT frameworks, API integrations, and event streaming (Kafka/EventHub).
• Understanding of data modeling, data vault, star/snowflake schema, and data products.

Required Skills

• Ability to write clean, optimized SQL and pipeline code.
• Strong analytical and troubleshooting skills with ability to diagnose poorly performing workloads.
• Ability to translate business requirements into Snowflake data structures and pipelines.
• Strong collaboration and communication skills with cross-functional technical and non-technical teams.
• Ability to prioritize workload in a fast-paced environment with shifting needs.

Preferred Qualifications

• SnowPro Core, SnowPro Advanced, or Snowflake Solutions Architect certifications.
• Experience with Snowflake's AI/ML stack (Cortex, Snowpark ML, Vector Stores).
• Experience with DBT Cloud, GitHub Actions, CI/CD for data pipelines.
• Experience enabling AI workflows in Snowflake (RAG, inference endpoints, embeddings).
• Experience in higher education or other federated organizations.

Key Accountabilities & responsibilities

Snowflake Development & Optimization

• Build and maintain Snowflake schemas, tables, dynamic tables, materialized views, file formats, stages, and secure data sharing structures.
• Develop performant SQL/ELT code using Snowflake best practices, including clustering, micro-partitioning, and query profiling.
• Monitor workloads and optimize compute usage, warehouses, and storage for cost and performance efficiency.

Data Pipeline & Integration Development

• Design and develop ELT pipelines with Fivetran, DBT, Informatica, or equivalent tools.
• Integrate legacy and cloud systems (Banner, Workday, Salesforce, ServiceNow, Azure) into Snowflake.
• Build scalable ingestion patterns including batch, event-driven, and incremental data pipelines.

AI & Advanced Snowflake Capabilities

• Work with Snowflake Cortex for AI/ML workloads including embedding generation, LLM inference, and cognitive search.
• Develop Snowpark Python transformations, UDFs, and machine-learning features.
• Implement vectorized storage, model-serving patterns, and AI-ready data transformations.
• Support RAG/semantic search pipelines and agentic workflows leveraging Snowflake-native AI features.

Data Quality, Governance & Security

• Implement data quality rules, metadata tagging, lineage tracking, and documentation.
• Manage roles, RBAC, masking policies, and compliance with FERPA/GDPR/security controls.
• Ensure all datasets and products meet consistency, reliability, and discoverability standards.

Collaboration & Cross-Functional Support

• Partner with data architects, data engineers, analytics teams, and domain stewards.
• Support academic and administrative units requiring Snowflake solutions.
• Troubleshoot, monitor, and improve existing data products in partnership with business and IT stakeholders.