Snowflake Engineer

Remote • Posted 8 hours ago • Updated 8 hours ago
Contract W2
6 Months
Occasional Travel Required
Remote
Depends on Experience
Fitment

Dice Job Match Score™

👤 Reviewing your profile...

Job Details

Skills

  • Snowflake

Summary

Hello Kalyan,
Hope you are doing well. Let me know if you are open to new job opportunities.
 
 

Snowflake Engineer

DURATION

6+ months

LOCATION

Miami FL/Remote with occasional travel to South Florida

REQUIRED SKILLS

Please see below:

 

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

 
 

“STRENGTHS:

+ Good performance optimization expertise - demonstrated ability to identify inefficiencies and implement dramatic improvements (converting batch API calls to array-based operations, reducing execution time significantly)

+ Strong architectural thinking with real-world experience designing Snowflake data solutions for complex enterprise environments including semi-structured data handling, clustering strategies, and metadata management

+ Proven production ownership with evolution from basic monitoring to modern DataOps practices (Nagios --> dataops.live, dbt), showing continuous improvement mindset

+ Practical AI/ML data readiness experience - built semantic layers and implemented user-centric prompting approaches for AI-driven query generation

+ Good Python engineering skills beyond scripting - developed reusable UDFs for geo-spatial indexing (MGRS) that mirror native Snowflake capabilities

+ Strong data quality principles - commitment to ensuring misleading or incomplete data is never exposed to users

 

AREAS FOR IMPROVEMENT:

- Deepen knowledge of Snowflake-specific features like streams, tasks, and dynamic tables for incremental processing

- Strengthen Streaming/micro-batch pipeline experience, particularly around replay mechanisms, idempotency patterns, and formal backfill strategies

- Explore strategies for Semi-structured data handling wrt schema evolution and variant usage”

 

 

Thanks and Regards,

 

Rajesh Miryala

 

Sr IT Recruiter. 

Gemini Consulting Services

3636 S Geyer Rd # 270, St. Louis, MO 63127, United States

Desk: 

Fax:

 

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 10411403
  • Position Id: 9024532
  • Posted 8 hours ago
Contact the job poster
MB

Mahesh Badam

Recruiter @ Gemini Consulting Services
Create job alert
Set job alertNever miss an opportunity! Create an alert based on the job you applied for.

Similar Jobs

Remote

3d ago

Easy Apply

Full-time

Depends on Experience

Remote or Easton, Pennsylvania

Today

Full-time

Remote

22d ago

Easy Apply

Third Party, Contract

$75 - $80

Remote or Hybrid in New York, New York

13d ago

Easy Apply

Contract, Third Party

Depends on Experience

Search all similar jobs