Sr Lead Data Engineer – Data & Agentic AI

About Northern Trust

As a global leader in innovative wealth management, asset servicing, asset management and banking services, Northern Trust (Nasdaq: NTRS) is proud to guide the world’s most successful individuals, families, corporations and institutions.

Since 1889, we have aligned our efforts with our three guiding Principles That Endure: Service, Expertise, and Integrity. Together, they reflect the three cornerstones of business conduct which we strive to instill in our employees, whom we call partners, and to provide to our clients and the communities we serve worldwide.

With more than 135 years of financial experience and over 24,000 partners, we serve the world’s most sophisticated clients using leading technology and exceptional service.

Northern Trust Asset Management (NTAM) is seeking a hands-on Senior Lead Data Engineer to design, build, and operate modern data and AI-driven solutions. This is a deeply technical role focused on building scalable data platforms and applying Agentic AI to solve real-world data engineering, quality, and analytics challenges.

Role Overview

This role sits within NTAM’s Data & AI engineering organization and partners closely with investment, operations, and technology stakeholders. The successful candidate will work hands-on across the full data lifecycle—data ingestion, modeling, validation, analytics, and AI augmentation—using Snowflake as the core data platform.

Key Responsibilities

Design, build, and maintain scalable data pipelines, data models, and analytics solutions on Snowflake.

Lead hands-on development of Agentic AI solutions within the data platform, focused on automation and augmentation of data engineering and analytics workflows.

Implement intelligent data quality checks, QA automation, reconciliation, and validation frameworks.

Build automated impact analysis capabilities to assess downstream effects of changes to data pipelines, schemas, and business logic.

Translate business and operational data problems into well-defined agent-based architectures with clear decision boundaries and human-in-the-loop controls.

Integrate AI agents with enterprise data services including Snowflake, APIs, metadata and lineage services, and analytics layers.