Analytics Engineer – Data Platform
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Job Description
Analytics Engineer building Snowflake and dbt models for Aline’s senior-living resident lifecycle platform. Creating governed data foundations for BI reporting and AI-driven care outcomes.
Responsibilities:
- Own and extend dbt models on Snowflake using medallion architecture (bronze/silver/gold) patterns
- Manage CDC-based ingestion pipelines using Fivetran and similar tools from transactional systems into Snowflake
- Evolve Snowflake and dbt models into a governed semantic layer for reporting and analytics
- Build reliable, well-tested gold-layer datasets for BI reporting and downstream AI/agentic workloads
- Apply star schemas and SCDs to support multi-tenant SaaS reporting
- Collaborate with data operations, platform engineering, product teams, and others
- Write dbt tests and documentation
- Monitor pipeline health and data freshness and build alerting
- Monitor Snowflake warehouse usage and query performance and tune models and warehouses
- Work with Product, Sales, and Customer Success stakeholders to translate reporting requests into well-modeled data
- Support row-level security and data access design for multi-tenant customer data isolation
Requirements:
- Strong SQL fundamentals and hands-on Snowflake experience
- Production experience with dbt; building, testing, and maintaining models at scale
- Experience with CDC pipelines and ELT tools like Fivetran
- Familiarity with Power BI, ideally including semantic model / measures work
- Understanding of medallion architecture and how gold-layer data supports both BI and AI use cases
- Experience working in a multi-tenant SaaS environment strongly preferred
- Comfortable with git-based version control and CI/CD for analytics code (dbt/SQL changes reviewed and deployed like software)
- Working knowledge of an orchestration tool (dbt Cloud, Airflow, Dagster, or similar) for scheduling and monitoring pipelines
- Python for scripting and automation around data workflows
- Strong communicator who can translate technical modeling decisions for non-technical stakeholders
- Comfortable in a lean team environment where you may own a problem end-to-end rather than a narrow slice of it
- Exposure to data ontology/semantic modeling for AI/agentic workloads (bonus)
- Familiarity with healthcare data privacy considerations, such as HIPAA (bonus)
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