This role is hands-on and ownership-driven, with a strong focus on:
Ingesting data from enterprise SaaS platforms Building scalable Snowflake ELT pipelines
Designing analytics-ready data models Owning the initial Snowflake platform foundations in collaboration with architecture leadership The ideal candidate has deep experience integrating CRM and marketing systems via APIs, is comfortable operating production grade data pipelines, and can make sound decisions around performance, cost, and reliability.
Key Responsibilities
1. SaaS Data Ingestion & Integration (Primary Focus) Design, build, and maintain
robust data ingestion pipelines from enterprise SaaS platforms, including:
Salesforce (CRM)
NetSuite (Finance)
Marketing and RevOps tools such as Marketo, 6sense, Gong
SharePoint (files, metadata, permissions)
Develop API-based ingestion frameworks handling:
Authentication and authorization
Pagination, rate limits, retries, and failures
Incremental loads, soft deletes, and historical tracking
Schema evolution and upstream source changes
2. Snowflake Data Engineering & Transformation Design and implement scalable
ELT pipelines within Snowflake Write high-quality, optimized SQL for complex
transformations Build and manage data layers including raw, staged, and curated datasets
Optimize Snowflake warehouses, storage, and query performance with a strong focus on
cost efficiency
3. Data Modeling & Analytics Enablement Design and maintain analytical data
models including:
Fact and dimension tables
Star and snowflake schemas
Slowly Changing Dimensions (SCD Type 1 and Type 2) Ensure data models support
reporting, dashboards, and research analytics Partner with analytics and research teams to
deliver analytics-ready, well documented datasets
4. Platform Ownership, Reliability & Governance Own end-to-end pipeline
reliability including scheduling, monitoring, alerting, and recovery Implement data quality
checks for accuracy, completeness, and freshness
Support Snowflake platform foundations including:
Warehouse and environment strategy (dev/test/prod)
Role-based access control (RBAC) Secure handling of sensitive HR and finance data (PII)
Troubleshoot and resolve data issues across ingestion, transformation, and consumption
layers
5. Collaboration & Engineering Best Practices Collaborate closely with analytics,
research, product, and technology stakeholders to translate business needs into data
solutions Contribute to data platform architecture discussions and continuous
improvement initiatives.
Maintain clear documentation for pipelines, data models, and data flows
Follow modern engineering practices including Git-based version control and CI/CD
workflows