Snowflake Engineer

Role Overview: Senior Snowflake Data Engineer

Seniority & Expectations

• Role is explicitly Senior-level
• Expected to:
• Work independently
• Drive initiatives forward without needing constant direction
• Contribute to architecture decisions, not just execute tickets
• Serve as the first Snowflake/Data Engineering hire
• Long-term, strategic need—not short-term staff augmentation

Data Platform & Architecture (Very Key Section)

How Snowflake Is Being Used

• Snowflake is positioned as a Data Lakehouse, not a traditional data warehouse
• Data sources include:
• Structured transactional data (e.g., casino systems)
• Semi-structured streaming data (e.g., JSON)
• Web and telemetry data

Architecture Approach

• Medallion Architecture:
• Raw layer: Ingest everything “as-is,” no transformation
• Silver layer: Cleaned, formatted, standardized (e.g., Parquet)
• Curated layer: Domain-focused datasets aligned to business processes
• Data is curated for AI/ML consumption, not BI dashboards

Analytics & Consumption Model

• Not focused on:
• Kimball modeling
• Traditional BI-first dashboards
• Primary consumers:
• LLMs
• Machine learning and AI models
• End users will query data conversationally via LLMs
• Emphasis on:
• Clean, accurate, well-modeled data
• Strong semantic layer to support ML/LLM usage

Important: The Data Engineer is not expected to build ML or LLM models, but must collaborate closely with ML engineers to define schemas and the semantic layer.

Data Engineering Responsibilities

Pipelines

• Combination of:
• Existing pipelines (maintenance)
• New pipeline development
• Supports both:
• Batch processing (micro-batches every few minutes)
• Streaming data (near real-time ingestion)

Data Characteristics

• Semi-structured and structured data
• High-frequency ingestion
• Streaming via brokers, with configurable consumption intervals (seconds to minutes)

Key Experience Required

• Hands-on Snowflake expertise
• Complex data pipeline engineering
• Streaming + micro-batch architectures
• Semi-structured data processing

Strong understanding of distributed data architectures