Description:

Architect and build foundational data pipeline infrastructure for a decentralized Data Mesh strategy.
Support transition from legacy on-prem architecture to a modern cloud data platform using Snowflake.
Design reference architectures and reusable pipeline patterns for batch, event-based, and streaming data movement.
Ensure data platform is "AI-ready" by building high-quality pipelines and robust semantic layers.
Lead a small data engineering team and mentor decentralized analysts to empower self-service data product creation.

Requirements:

5–7 years of dedicated data engineering experience with a proven track record of building production-grade data pipelines.
Advanced, hands-on experience with Snowflake architecture, performance tuning, and data sharing.
Proven experience in moving data across hybrid environments (on-prem to cloud) using batch, streaming, and event-driven patterns.
Experience building or maintaining semantic layers for AI and natural language search features.
Strong Python skills in cloud environments and strong SQL capabilities.
Exposure to dbt and experience with Azure data tools preferred; familiarity with AWS data ecosystems is a plus.
Collaborative leadership style with a passion for teaching and establishing governance.

Opportunity to work with leading financial institutions and clients on AI adoption.
Direct collaboration with clients, engineers, and AI specialists to achieve measurable business outcomes.
A supportive environment for teaching and facilitating new ways of working.

Skills