Snowflake Data Engineer (w2 position)

Job Title: Snowflake Data Engineer

Location: Hybrid Onsite (Hollywood, FL)- Try for Locals first, Secondary someone who can relocate within from Florida.

Duration: 6 months + CTH

Job Description:

To align on requirements, scope, and hiring approach for a Senior Snowflake Data Engineer role, including technical expectations, location preferences, engagement model, and next steps for sourcing and approvals.

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 US-based 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

Location & Work Model (Critical Hiring Constraint)

Preferred Location

• Florida-based candidates are strongly preferred
• Ideal scenario:
• Onsite presence, especially initially
• In-office collaboration with data engineering team
• Hybrid may be considered if Florida-based

Time Zone & Availability

• Must work EST hours
• Not a “fully asynchronous / flexible hours” remote role
• Expectation of strong availability and collaboration during core hours

Client emphasized past difficulty onboarding and enabling senior resources fully remote.

Engagement Model & Duration

• Role is intended to be:
• Long-term
• Strategic
• High-investment
• Not looking to rotate resources every 12–18 months
• Ramp-up expected to take 6–8 months for full productivity

Conversion Potential

• Client expressed openness to conversion
• Long-term buy-in is viewed positively
• Commercial terms and timeline to be discussed further

Interview Timing & Urgency

• Client is eager to move quickly once approvals are in place
• Technical interviewers (including Assaf) are:
• Available immediately
• Flexible on scheduling
• Process will move once commercials and approvals are finalized

Key Risks / Watchouts Identified

• Finding senior Snowflake engineers locally in Florida
• Ensuring candidate seniority aligns with:
• Architectural ownership
• Independence
• Complex streaming environments
• Balancing speed with approval dependencies (budget + SOW)

Overall Summary (Executive-Level)

This is a highly strategic, senior Snowflake Data Engineering role focused on building and evolving a Snowflake-based lakehouse that powers AI and ML use cases, not traditional BI. The success of this role is highly dependent on seniority, architectural capability, and in-person collaboration, with a strong preference for Florida-based candidates and long-term engagement.