AI Data Innovation Engineer

US Bank; Minneapolis (MN); On-site; USD 150,000 - 190,000; Full time

Job summary

U.S. Bank is seeking a senior AI Data Innovation Engineer to advance enterprise AI capabilities by building and operationalizing data products and agent-based experiences across Snowflake, Databricks, and cloud ecosystems.

You will translate experiments into reusable patterns, collaborate with governance and security teams, and guide teams toward safe, scalable AI solutions in a bank-wide data program. This role requires hands-on coding, deep platform knowledge, and a production-focused mindset

Qualifications

• Experience delivering AI-enabled data products at scale.
• Practical design, validation and operationalization of AI-enabled data products and agent-based solutions.
• Collaboration with governance, risk, security, and data platform teams.

Responsibilities

• Prototype, validate, and operationalize AI capabilities using governed data products and standardized semantic models.
• Develop and evolve reusable AI enablement patterns and reference architectures.
• Translate experiments into adoption guidance that enables teams to deliver safely at scale.

Skills

AI data platforms

Agent orchestration

Semantic grounding

Observability

Governance & security

Tools

Snowflake

Databricks

Snowpark

Genie Spaces

Cortex

Job description

Job Description

At U.S. Bank, we're on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at-all from Day One.

We are seeking a highly skilled AI Data Innovation Engineer to join the Data Innovation and Tools Rationalization team within the Enterprise Data Office. This senior individual contributor will bring hands-on experience building and operationalizing AI-enabled data products, agents, and reusable enablement patterns across modern enterprise data platforms. The role focuses on accelerating AI readiness, establishing safe and scalable patterns, and guiding teams through proven approaches for agent development, semantic grounding, governance, and production operations rather than relying on repeated trial-and-error learning.

About the Data Innovation and Tools Rationalization Team

We are the innovation and tooling engine for the Enterprise Data Office, focused on reusable patterns, accelerators, and tool rationalization that reduce friction and speed up delivery and adoption of governed data products.

Vision | Make data products and AI capabilities easier to build, safer to deploy, and faster to adopt across the bank.

Mission | Deliver reusable data product patterns, accelerators, and clear integration pathways that help teams ship data products faster while enabling safer AI adoption and reducing technology sprawl through disciplined tool evaluation and rationalization.

Values | In addition to U.S. Bank core values, we prioritize:

• Head high: We build with excellence. Our work is intentional, high quality, and designed to last, so we are always proud of what we deliver and comfortable standing behind it.


• Accountability Over Activity: We take end to end accountability, from problem framing through delivery, adoption, and outcomes.


• Strategic Intelligence: We think in systems, anticipate downstream impact, and collaborate to win as a pod, not as individuals.


• Relentless Craft: We are passionate about the work we do. Our drive comes from curiosity, purpose, and a genuine love of building impactful solutions.

About the Role

The AI Data Innovation Engineer is a senior individual contributor responsible for advancing enterprise AI capabilities from a data and data product standpoint. This role sits at the intersection of governed data products, agent-enabled experiences, AI platform engineering, and responsible innovation. The engineer acts as a force multiplier by building reference implementations, establishing reusable standards, and providing hands-on technical guidance to teams adopting AI across the Enterprise Data Office and broader organization.

Unlike traditional model-development or research-focused roles, this position emphasizes the practical design, validation, and operationalization of AI-enabled data products and agentic solutions. These capabilities are grounded in trusted enterprise data, semantic layers, business ontologies, metadata, reusable interfaces, and appropriate controls so they can be safely integrated and scaled across Snowflake, Databricks, Microsoft, and cloud ecosystems.

The ideal candidate has already delivered AI-enabled solutions beyond isolated proofs of concept and can help other teams move from experimentation to repeatable delivery. The candidate combines technical depth in modern data and AI platforms with practical experience in agent orchestration, semantic grounding, guardrails, evaluation, observability, deployment, and enterprise operating models.

Key Activities

Key Responsibilities Include:

• Prototype, validate, and operationalize AI capabilities that leverage governed enterprise data products, standardized semantic models, business ontologies, metadata, shared feature representations, and reusable data interfaces.


• Develop and evolve reusable AI enablement patterns including Snowpark workloads, Cortex AI functions, retrieval-augmented generation, tool-enabled agents, agent orchestration, and human-in-the-loop workflows aligned with enterprise platform standards.


• Design and validate conversational data and agent-enabled experiences using capabilities such as Snowflake Cortex Agents and Databricks Genie Spaces, Genie agents, and Genie-powered applications, along with comparable enterprise AI platforms.


• Translate successful experiments into reference architectures, reusable components, implementation playbooks, and adoption guidance that enable teams to deliver safely without relearning foundational patterns for each use case.


• Partner with data governance, risk, security, and control teams to define and implem