Principal Engineer – AI Engineering Productivity Lead

Wells Fargo

. Design, develop, and maintain scalable data pipelines integrating engineering, delivery, quality, workforce, and AI adoption data .

Posted 10/14/2026full-timeUnited StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced expertise in SQL, Power BI, DAX, and Power Query for developing scalable data pipelines and analytics solutions. Proven ability to analyze engineering productivity metrics and translate complex data into actionable insights for leadership decision-making.

Highest-signal resume keywords

Advanced SQL ExpertisePower BI Dashboard DevelopmentData Pipeline ConstructionData Governance KnowledgePython Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLDAXPower QueryData ModelingData TransformationData Quality MonitoringAI Adoption MeasurementStatistical AnalysisCI/CD AnalysisAnomaly Detection

Soft Skills

Stakeholder InfluenceLeadership SkillsContinuous Improvement FocusFacilitation SkillsDecision-Making

Tools & Technologies

Power BICloud Data PlatformsAPIsOrchestration ToolsEnterprise Analytics Environments

Industry Keywords

Data EngineeringBusiness IntelligenceDORA MetricsEngineering EffectivenessSelf-Service Analytics

Tech Stack

Tools & technologies

CloudPythonSQL

About the role

Key responsibilities & impact

Design, develop, and maintain scalable data pipelines integrating engineering, delivery, quality, workforce, and AI adoption data
Build reusable data models, SQL transformations, validation processes, and reconciliation controls
Partner with enterprise data owners and platform teams on data ingestion, integration, governance, lineage, and reporting standards
Develop and maintain executive-level Power BI dashboards, scorecards, semantic models, and self-service analytics
Create governed data models, metrics, and KPIs using SQL, DAX, Power Query, and enterprise data platforms
Create trusted metrics and actionable insights to support leadership decisions and improve engineering capacity
Analyze engineering productivity, DORA metrics, developer experience, delivery flow, quality, automation, and AI adoption
Perform root-cause, trend, benchmarking, segmentation, and impact analyses
Translate technical and business data into executive insights, recommendations, and decision-support materials
Define measurement frameworks for AI-enabled engineering solutions
Partner with engineering leaders on pilots, instrumentation, feedback loops, and continuous improvement strategies
Leverage automation and AI-assisted analytics to reduce manual reporting and accelerate insight generation
Develop metric guidance, enablement materials, demonstrations, and best practices
Establish metric definitions, data-quality standards, automated controls, anomaly detection, reconciliation processes, and reporting governance
Ensure compliance with data governance, privacy, security, records management, risk, and technology standards
Maintain transparency and auditability across data sources, calculations, measures, dashboards, and executive reporting

Requirements

What you’ll need

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of work experience, training, military experience, or education
7+ years of experience in data engineering, analytics, business intelligence, or related technical disciplines
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
Ability to work in office
Not eligible for visa sponsorship

Benefits

Comp & perks

Hybrid work schedule
Equal opportunity employment
Disability accommodation during the recruitment or interview process
Drug-free workplace