Description

Responsibilities

• Design, develop, and optimize data solutions within a Snowflake-based Data Lakehouse environment.
• Build and maintain scalable data pipelines supporting both batch and near real-time streaming workloads.
• Develop and enhance data ingestion frameworks for structured, semi-structured, and telemetry data sources.
• Implement and maintain Medallion Architecture layers, including Raw, Silver, and Curated datasets.
• Create clean, trusted, and well-modeled datasets for downstream AI and machine learning consumption.
• Partner closely with ML Engineers, Data Scientists, and business stakeholders to define schemas and support semantic data layer development.
• Contribute to data architecture decisions, platform standards, and best practices.
• Support existing Snowflake pipelines while driving the development of new capabilities and workflows.
• Monitor, troubleshoot, and optimize data processing performance, scalability, and reliability.
• Ensure data quality, governance, and consistency across the platform.

Data Platform Environment

The organization utilizes Snowflake as a Data Lakehouse platform, not a traditional data warehouse.

Data sources include:

• Structured transactional systems
• Semi-structured JSON data
• Web, event, and telemetry data
• High-frequency streaming data sources

The platform follows a Medallion Architecture approach:

• Raw Layer – ingest data as received
• Silver Layer – cleanse, standardize, and transform data
• Curated Layer – domain-driven datasets optimized for business and AI use cases

This environment is designed primarily for:

• Large Language Models (LLMs)
• Machine Learning platforms
• AI-driven applications

Key Skills

Education

Any Gradute