Snowflake Data Engineer

Snowflake Data Engineer

Role Overview

A global organization is seeking a Snowflake Data Engineer to design, build, and optimize modern cloud-based data solutions. This role focuses on developing scalable data pipelines, implementing efficient ELT processes, and leveraging advanced platform capabilities to support analytics and emerging AI use cases.

The ideal candidate has hands-on experience with Snowflake and cloud platforms, with the ability to deliver high-performance, secure, and well-governed data environments.

Key Responsibilities

Data Engineering & Platform Development

Design, develop, and maintain end-to-end data pipelines for ingestion, transformation, and data delivery.

Implement scalable data solutions using Snowflake across storage, processing, and access layers.

Build reusable components, frameworks, and user-defined functions (UDFs) using SQL and Snowflake-native capabilities.

Develop optimized ELT workflows leveraging modern data engineering practices.

Performance Optimization & Monitoring

Conduct performance tuning, workload analysis, and query optimization.

Utilize clustering, caching, and partitioning strategies to improve performance and efficiency.

Monitor pipeline execution and ensure adherence to SLAs and performance benchmarks.

Data Governance & Security

Establish and maintain data governance frameworks, including role-based access control (RBAC) and data masking policies.

Ensure compliance with data security and privacy standards.

Implement auditing and monitoring mechanisms for secure data access.

Cloud & Integration

Develop and deploy solutions within cloud environments such as

Amazon Web Services

Microsoft Azure

Google Cloud Platform

Integrate Snowflake with upstream and downstream systems for seamless data flow.

Advanced Data & AI Enablement

Support advanced analytics and AI initiatives using Snowflake’s native capabilities.

Implement modern features such as dynamic tables, streaming ingestion, and data sharing.

Enable emerging AI/GenAI use cases using platform-native tools and workflows.

Qualifications and Experience

At least 2 years of experience as a Data Engineer or in a Snowflake-focused role.

Strong hands-on experience with Snowflake architecture and internal mechanisms (e.g., clustering, caching, performance tuning).

Proficiency in SQL and Snowflake scripting.

Experience working with at least one major cloud platform (AWS, Azure, or GCP).

Technical Skills

Expertise in

Snowflake

• SQL and ELT pipeline development
• Data modeling and data warehousing principles

Familiarity with

• Streaming and ingestion tools (e.g., Snowpipe)
• Advanced Snowflake features (e.g., dynamic tables, data sharing)
• Cloud-native data architectures

Preferred Qualifications

• Snowflake certification (e.g., SnowPro Core)
• Exposure to AI/ML or GenAI use cases within data platforms
• Experience implementing data governance and security frameworks
• Ability to design scalable and reusable data engineering solutions

Skills and Competencies

• Strong analytical and problem-solving abilities
• Excellent communication skills, with the ability to translate technical concepts to business stakeholders
• Attention to detail and commitment to data quality
• Ability to work independently and in cross-functional teams
• Adaptability in a fast-paced, evolving technology environment

Why Consider This Role

• Opportunity to work with modern cloud data platforms and architectures
• Exposure to advanced analytics and AI-driven data use cases
• Hands-on experience with Snowflake’s latest capabilities
• Collaborative environment focused on innovation and scalability