Related skills
snowflake databricks rag iceberg lakehouse📋 Description
- Establish standards for schemas, naming, events, and data representation.
- Define data persistence across OLTP/OLAP/lakehouse and feature stores.
- Set best practices for partitioning, indexing, storage tiering, and lifecycle.
- Drive cost reduction via architectural optimization and deduplication.
- Lead technology evaluations and decision frameworks (Databricks vs Snowflake, Delta vs Iceberg).
- Define when to use batch, micro-batch, streaming, or event-driven architectures.
🎯 Requirements
- 10+ years in data architecture, 5+ in multi-domain environments.
- Deep expertise in multi-layer persistence and platforms: Snowflake, Databricks, Delta, Hudi, Iceberg.
- Strong knowledge of partitioning, sharding, storage tiering, and workload isolation.
- Experience enabling production AI/ML, including semantic layers, feature stores, and RAG.
- Hands-on fluency with tables, events, APIs, and data structures aligned to business models.
- Experience evaluating tech on cost, performance, scalability, governance, and ops.
- Crisp communication across technical and executive audiences; healthcare data standards knowledge (FHIR, USCDI) and HIPAA familiarity.
🎁 Benefits
- Benefits starting from Day 1
- Retirement Plan Matching
- Flexible Paid Time Off
- Wellness Support Programs and Resources
- Parental & Caregiver Leaves
- Fertility & Adoption Support
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