Senior Data Product Accelerator Lead – Data Innovation & Tools

Senior Data Product Accelerator Lead – Data Innovation & Tools

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.

The Senior Data Product Accelerator Lead is a senior contributor responsible for accelerating enterprise adoption of modern data platforms, tools, and data product patterns. The role lies at the intersection of product enablement, technical strategy, and execution, acting as a force multiplier for delivery teams across the Enterprise Data Office and the broader bank.

• Lead the design and evolution of reusable data product accelerators, reference architectures, and integration patterns across platforms such as Snowflake, Databricks, and Power Platform.
• Serve as a senior technical and product advisor to delivery teams adopting enterprise data platforms and tools.
• Identify recurring friction points in data product delivery and design scalable solutions that remove those barriers.
• Partner with platform, architecture, and governance teams to evaluate, test, and validate data and analytics tools.
• Lead proofs of concept and comparative assessments to inform tool rationalization and standardization decisions.
• Translate platform capabilities into clear adoption pathways, playbooks, and usage guidance for teams.
• Apply product thinking to data and analytics capabilities, ensuring solutions are designed for usability, adoption, and measurable impact.
• Define success metrics for accelerators and enablement efforts, tracking adoption, reuse, and productivity gains.
• Continuously refine assets based on feedback, usage data, and evolving enterprise needs.
• Work closely with data engineers, analytics engineers, architects, data product owners, and governance partners to align solutions with enterprise data strategy.
• Act as a connector across teams, ensuring consistency while respecting domain specific needs.
• Influence engineering and product standards through credibility, hands‑on expertise, and demonstrated results.
• Evaluate, test, and experiment with emerging data and AI tools, platforms, and services.
• Document project outcomes, transition plans, adoption guides, and solution usage scripts to support enterprise rollout.

• Deep understanding of financial institution/Banking concepts.
• Strong understanding of modern data engineering concepts, including batch and streaming data processing, data modeling, and data product design.
• Familiarity with enterprise data ecosystems and shared platform models.
• Ability to assess trade‑offs across tools, architectures, and implementation approaches.
• Strong analytical and problem‑solving skills with a focus on root cause analysis and optimization.
• Operates as a leader without relying on formal authority.
• Comfortable working across ambiguity and shaping problems before solving them.
• Strong communicator who can engage both deeply technical teams and senior stakeholders.

• Familiarity with orchestration and workflow management tools.
• Strong hands‑on experience with modern data platforms such as Snowflake and Databricks, with the ability to translate technical capabilities into reusable patterns.
• Proficiency in SQL and Python, with sufficient depth to prototype, validate, and guide implementation approaches.
• Experience with orchestration, CI/CD, and cloud native patterns as they relate to scalable enablement.
• Familiarity with data quality, observability, security, and governance concepts as they impact product adoption.
• Exposure to AI and ML enablement, including how data products support analytics, modeling, and emerging AI use cases.

• Bachelor’s Degree in a quantitative field such as computer science, engineering,…