Principal Engineer - AI Engineering Productivity Lead

Wells Fargo•Charlotte, NC

1d•Hybrid

About The Position

Wells Fargo is seeking a Principal Engineer who will function as the Chief Operating Office's (COO) Technology Group's AI Engineering Productivity Lead. This individual will be a highly technical leader within the COO Technology Transformation Office responsible for engineering the data, analytics, and insight capabilities that enable measurable improvements in engineering productivity, quality, speed, and AI adoption. The role integrates data from engineering and delivery ecosystems, establishes trusted metric definitions, develops scalable analytical solutions, and converts complex delivery signals into clear recommendations for technology leaders. The role combines hands-on data engineering and business intelligence expertise with engineering excellence leadership. It owns the end-to-end analytical lifecycle, from source assessment, data quality and transformation logic through semantic modeling, Power BI visualization, advanced analysis, trend identification, executive insights, and enablement. The individual will partner with engineering leaders and data owners to improve data reliability, expand self-service access, and ensure insights drive action rather than reporting alone.

Requirements

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
7+ years of experience in data engineering, analytics, business intelligence, or related technical disciplines.

Nice To Haves

Advanced expertise in SQL, Power BI, DAX, Power Query, and semantic data modeling.
Experience building data pipelines, data transformations, and enterprise analytics solutions.
Proficiency in Python or similar languages for automation, analytics, and data quality monitoring.
Experience analyzing software engineering, CI/CD, delivery, quality, and developer productivity metrics.
Strong knowledge of data modeling, governance, lineage, and data quality best practices.
Ability to transform complex business challenges into actionable insights and recommendations.
Proven ability to influence stakeholders and drive adoption across large, matrixed organizations.
Experience with cloud data platforms, APIs, orchestration tools, and enterprise analytics environments.
Knowledge of DORA metrics, engineering effectiveness, developer productivity, and delivery frameworks.
Experience measuring AI-enabled engineering adoption, utilization, and business impact.
Familiarity with statistical analysis, forecasting, trend analysis, and anomaly detection.
Experience modernizing reporting solutions and enabling self-service analytics.
Strong facilitation and leadership skills with a focus on continuous improvement and data-driven decision-making.

Responsibilities

Design, develop, and maintain scalable data pipelines integrating engineering, delivery, quality, workforce, and AI adoption data from approved enterprise sources.
Build reusable data models, SQL transformations, validation processes, and reconciliation controls to ensure data accuracy, integrity, traceability, and operational efficiency.
Partner with enterprise data owners and platform teams to establish sustainable data ingestion, integration, governance, lineage, and reporting standards.
Develop and maintain executive-level Power BI dashboards, scorecards, semantic models, and self-service analytics supporting engineering productivity, AI adoption, quality, delivery performance, and organizational capacity.
Create governed data models, metrics, and KPIs using SQL, DAX, Power Query, and enterprise data platforms while optimizing performance, usability, accessibility, and reliability.
Create trusted metrics and actionable insights that enable leaders to make informed decisions, remove delivery bottlenecks, increase engineering capacity, and quantify the value of AI-enabled ways of working.
Analyze engineering productivity, DORA metrics, developer experience, delivery flow, quality, automation, and AI adoption to identify trends, opportunities, risks, and improvement actions.
Perform root-cause, trend, benchmarking, segmentation, and impact analyses to measure the effectiveness of engineering transformation initiatives and AI-enabled tools.
Translate complex technical and business data into actionable executive insights, recommendations, and decision-support materials.
Define and implement measurement frameworks for AI-enabled engineering solutions, including adoption, utilization, effectiveness, value realization, and success metrics.
Partner with engineering leaders to design pilots, instrumentation, feedback loops, and continuous improvement strategies for emerging technologies and engineering capabilities.
Leverage automation and AI-assisted analytics to reduce manual reporting, accelerate insight generation, and surface actionable business exceptions.
Develop metric guidance, enablement materials, demonstrations, and best practices that promote consistent engineering measurement and data-driven decision-making.
Establish common metric definitions, data-quality standards, automated controls, anomaly detection, reconciliation processes, and reporting governance practices.
Ensure compliance with data governance, privacy, security, records management, risk, and technology standards while identifying and remediating data quality gaps and source-system limitations.
Maintain transparency and auditability across data sources, calculations, derived measures, dashboards, and executive reporting to support trusted business outcomes.