Senior Data Product Accelerator Lead – Data Innovation & Tools

U.S. Bank • Minneapolis, MN

1d • Hybrid

About The Position

We are seeking a highly skilled Senior Data Product Accelerator Lead to join the Data Innovation and Tools Rationalization team within the Enterprise Data Office. This role plays a critical hands-on leadership role in advancing the modernization of enterprise data capabilities by designing, proving, and scaling reusable data product accelerators, integration patterns, and enablement frameworks aligned with the Enterprise Data Strategy. The role focuses on accelerating platform adoption, improving delivery consistency, and reducing friction across teams through disciplined tool evaluation, product thinking, and enterprise scale enablement.
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.
The Senior Data Product Accelerator Lead is a senior contributor responsible for accelerating enterprise adoption of modern data platforms, tools, and data product patterns. This role sits at the intersection of product enablement, technical strategy, and execution, acting as a force multiplier for delivery teams across the Enterprise Data Office and broader bank. Unlike a traditional engineering role, this position focuses less on day-to-day pipeline development and more on designing, proving, and scaling reusable accelerators, integration patterns, and operating guidance that materially improve time to value, consistency, and quality of data products. The ideal candidate brings deep technical credibility, strong product thinking, and the ability to influence across teams without formal authority. This role plays a critical part in reducing fragmentation, improving developer and analyst productivity, and ensuring that modern data capabilities are adopted safely and effectively at scale.

Requirements

• 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 tradeoffs 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 stakeholder.
• 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, data science, mathematics, or statistics.
• 10+ years of experience across data engineering, analytics engineering, platform enablement, or data product roles.

Nice To Haves

• Demonstrated experience influencing adoption of shared platforms, tools, or standards in a large enterprise environment.
• 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 analytical, organizational, problem-solving, and project management skills.
• Hands-on experience with programming languages such as Python and SQL.
• Proficiency with big data technologies including Hadoop, Hive, and Spark.
• Expertise in visual analytics tools such as Power BI, Tableau, or equivalent platforms.
• Experience with Power Platform tools such as Power Automate and Power Apps
• Proven track record in automating and optimizing ETL processes at scale.
• Excellent written and verbal communication skills for documenting technical processes and engaging with cross-functional teams and present to senior management.

Responsibilities

• 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.
• Evaluating, testing, and experimenting with emerging data and AI tools, platforms, and services.
• Documenting project outcomes, transition plans, adoption guides, and solution usage scripts to support enterprise rollout.

Benefits

• Healthcare (medical, dental, vision)
• Basic term and optional term life insurance
• Short-term and long-term disability
• Pregnancy disability and parental leave
• 401(k) and employer-funded retirement plan
• Paid vacation (from two to five weeks depending on salary grade and tenure)
• Up to 11 paid holiday opportunities
• Adoption assistance
• Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees