AI Data Innovation Engineer, Data Innovation and Tools Rationalization

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.

Job Description

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 role plays a critical hands‑on role in advancing the adoption of AI‑enabled data capabilities by prototyping, validating, and operationalizing reusable AI Data patterns and enablement frameworks aligned with the Enterprise Data Strategy. The role focuses on accelerating AI readiness, enabling safe and scalable adoption, and reducing friction across teams through disciplined experimentation, platform integration, and enterprise‑scale AI enablement.

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 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 enterprise data products, AI enablement, and platform innovation, acting as a force multiplier for teams adopting AI‑enabled data products across the Enterprise Data Office and broader organization.

Unlike traditional model development or research‑focused roles, this position focuses on prototyping, validating, and operationalizing AI capabilities that are tightly coupled to governed enterprise data products, including standardized semantic models, feature representations, and reusable data interfaces. The AI Innovation Engineer works hands‑on to ensure that AI solutions are built on trusted data foundations and can be safely reused, integrated, and scaled across platforms.

The ideal candidate brings strong technical depth across modern data platforms and AI technologies, paired with a practical understanding of enterprise data products and operating models. This role plays a critical part in accelerating AI readiness, reducing fragmentation across AI implementations, and ensuring that innovative AI capabilities are delivered through consistent, well‑governed data products aligned with the Enterprise Data Strategy.

Key Activities

Key responsibilities include:

• Prototype and validate AI capabilities that leverage governed enterprise data products, including standardized semantic models, shared feature representations, and reusable data interfaces.
• Develop and evolve reusable AI enablement patterns such as Snowpark workloads, Cortex AI functions, retrieval‑augmented generation methods, and agent‑based approaches aligned with enterprise data platforms.
• Support data product AI readiness by partnering with data engineers and product teams to ensure data assets are structured, documented, and optimized for AI use cases.
• Translate experimental AI solutions into reference implementations, reusable patterns, and adoption guidance that can be safely reused across teams.
• Partner with data governance, risk, and control teams to ensure responsible AI alignment, documenting guardrails, constraints, and handoff artifacts required for scaling.
• Collaborate closely with the AI Center of Excellence to integrate validated AI patterns into enterprise AI experiences, including Chat USB.
• Evaluate and experiment with emerging AI tools, frameworks, and platform capabilities, conducting technical proofs of concept and comparative assessments.
• Identify recurring friction points in AI adoption and design scalable, data‑centric solutions that reduce complexity and risk.
• Document project outcomes, usage patterns, limitations, and operational considerations to support enterprise rollout and enablement.
• Work closely with data engineers, analytics engineers, architects, and data product owners to align AI solutions with enterprise data strategy and platform standards.
• Continuously refine AI assets based on feedback, usage data, and evolving enterprise needs.
• Influence data‑centric AI adoption through hands‑on expertise, technical credibility, and clearly articulated patterns rather than formal authority.

This role requires strong communication and collaboration skills, along with the ability to work effectively with stakeholders across data, technology, governance, and product teams. The successful candidate will bring strong technical fluency across modern data platforms, analytics tools, cloud capabilities, and emerging AI technologies, contributing to the Enterprise Data Strategy through hands‑on innovation and enablement.

Core Competencies:

Knowledge:

• Strong understanding of enterprise data products and how they enable analytics and AI use cases, including semantic models, shared feature representations, and reusable data interfaces.
• Solid understanding of modern data and AI enablement concepts, including retrieval‑augmented generation, prompt orchestration, agent‑based patterns, and model integration approaches grounded in governed data assets.
• Familiarity with enterprise data ecosystems and shared platform operating models, including how data products are built, governed, and reused at scale.
• Ability to assess tradeoffs across AI tools, data platforms, and architectural approaches, balancing innovation with scalability, security, and governance.
• Strong analytical and problem‑solving skills, with the ability to work effectively in ambiguous or emerging problem spaces.
• Comfortable operating as a senior individual contributor who influences outcomes through technical credibility rather than formal authority.
• Strong communicator able to engage effectively with data engineers, platform teams, governance partners, and AI practitioners.

Technical Competence:

• Hands‑on experience working with modern data platforms such as Snowflake and Databricks, with the ability to leverage data products as inputs to AI‑enabled workflows.
• Experience developing AI‑enabled solutions using Python and SQL, including prototyping, validation, and integration with enterprise data assets.
• Familiarity with Snowpark workloads, Cortex AI functions, or similar data‑native AI capabilities, with an emphasis on reuse and standardization.
• Experience implementing retrieval and semantic enrichment patterns that connect AI capabilities to governed enterprise data products.
• Understanding data quality, observability, security, and governance considerations as they relate to AI readiness and responsible adoption.
• Familiarity with cloud‑native services and APIs used to prototype and operationalize AI‑enabled data solutions.
• Experience documenting technical approaches, usage patterns, limitations, and handoff guidance to support enterprise adoption and scale.
• Exposure to CI/CD and deployment patterns for experimental and production‑ready AI workloads is a plus.

Basic Qualifications

• Bachelor’s Degree in a quantitative field such as computer science, engineering, data science, mathematics, or statistics.
• 7-10 years of experience across AI enablement, data engineering, analytics engineering, platform enablement, or data product roles.

Preferred Skills

• Demonstrated experience influencing adoption of shared platforms, tools, or standards in a large enterprise environment.
• AI/ML Model development experience
• Demonstrated experience prototyping and validating AI capabilities built on enterprise data products, including standardized semantic models and shared data interfaces.
• Experience developing reusable AI enablement patterns such as Snowpark workloads, Cortex AI functions, retrieval‑augmented generation methods, or agent‑based approaches.
• Technically proficient in model life cycle management, portfolio management, financial/budget management, and roadmap planning
• Proven track record of designing reusable components or standards adopted by multiple teams.
• Experience working across Snowflake, Databricks, and cloud ecosystems (Azure, AWS, or GCP).
• Experience working in regulated or large-scale enterprise environments preferred.
• Strong organizational skills with the ability to manage multiple initiatives concurrently.
• Deep understanding of banking and financial institution terms.
• Knowledge of banking regulation and requirements for regulatory reporting.
• Strong ana

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