Description

You will architect and maintain large-scale data pipelines and warehouse infrastructure, integrating structured and unstructured sources into LLM-powered products.

This role is hybrid.

Responsibilities

• Architect and optimize massive-scale data warehouses and lakehouses using Snowflake.
• Implement Model Context Protocol (MCP) or similar architectures to feed data into LLM applications.
• Define, monitor, and enforce data quality SLAs across all ingestion pipelines.
• Lead the integration of diverse data sources into the enterprise data ecosystem.
• Mentor junior engineers and contribute to enterprise-wide data infrastructure strategy.

Required Skills

• 8+ years of progressive experience in data engineering with demonstrated leadership.
• Expert-level SQL for building performant, scalable queries on massive datasets.
• Strong Python programming skills focused on distributed computing and data manipulation.
• Production-level experience with large-scale batch and streaming data processing.
• Hands-on experience with DBT for advanced data modeling and transformations.
• Deep knowledge of Snowflake data warehouse design and optimization.
• Proficiency with cloud platforms (GCP and/or AWS).
• Understanding of data architecture concepts: ETL/ELT, Data Mesh, and event-driven architectures like Kafka.

Preferred Skills

• Experience implementing MCP or similar architectures for LLM systems.

Key Skills

Education

Any Graduate