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Vice President, Technical Lead Data Engineering & Platforms

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Onsite

Vice President, Technical Lead Data Engineering & Platforms

Wells Fargo

Vice President, Technical Lead Data Engineering & Platforms

Skills

Data EngineeringReactProduct StrategyGrafanaData AnalysisMachine LearningAIPrometheus

1 month ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

16 September 2026

What they need


Job Summary

Wells Fargo is seeking a seeking a highly experienced Engineering Lead with deep expertise in Data Engineering, Data Warehousing, and Web Platform Architecture to drive our Estate Strategy Platform.

Responsibilities

In this role, you will:

  • Lead complex initiatives to develop infrastructure to provide solutions for business applications
  • Participate in various projects intended to continually improve or upgrade the infrastructure
  • Evaluate internal and external software solutions which could be leveraged to meet target state architecture goals
  • Review and analyze high impact outages to ensure the proper processes and procedures are in place to avoid problems in the future
  • Design, build, deploy and maintain infrastructure solutions through collaborative efforts with the team and third party vendors
  • Design, code, test, debug and document programs using Agile development practices
  • Make decisions in technical designs, implementation plans and identify project risks and resource requirements
  • Direct the daily risk and control flow of operations, focusing on policies, procedures and work standards to ensure success
  • Recommend courses of action to maintain cost effectiveness and achieve results
  • Collaborate and consult with peers, colleagues and managers to resolve issues and achieve goals
  • Interact with customer and vendor

Required Qualifications

  • 7+ years of Technology Infrastructure Engineering and Solutions experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications

  • 7+ years of experience in Data Engineering, Backend Engineering, or Platform Engineering, with at least 35 years in a technical leadership role or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
  • Expertise in data modeling, data warehousing, ETL/ELT frameworks, and realtime streaming architectures.
  • Strong proficiency in distributed systems, API architecture, and systems integration patterns.
  • Demonstrated experience building frontend dashboards and visualization platforms at scale (React, Angular, or similar).
  • Ability to engage senior stakeholders and provide clear, concise technical communication.

DCIM Product Strategy Evolution

  • Own and drive the technical execution of the product roadmap.
  • Align platform capabilities with business, capacity, and operational requirements.
  • Translate estate strategy, capacity, and operational needs into scalable product features and platform enhancements.

Data Engineering, Data Modeling Warehouse Architecture

  • Architect and lead the buildout of scalable data warehouses, data lakes, and ETL/ELT pipelines.
  • Design optimal data models to support analytics, observability, and operational reporting.
  • Ensure data quality, consistency, performance, and reliability through solid schema design, partitioning, indexing, retention, and archival strategies.
  • Establish governance, validation, and monitoring frameworks to ensure highfidelity capacity and estate insights.

Frontend, Visualization Web Platform Engineering

  • Architect and guide development of web applications, dashboards, and visualization tools for endusers in Operations, Capacity Planning, Facilities, and Leadership.
  • Ensure highly intuitive, performant UX for largescale datasets such as layouts, heat maps, asset views, and capacity insights.
  • Establish frontend development standards, component libraries, and scalable API patterns.
  • Drive rapid feature delivery without compromising quality, security, or maintainability.

Systems Integration Automation (preferred)

  • Lead integrations between the platform and systems.
  • Enable near realtime data flows, eventdriven pipelines, and automation to eliminate manual effort.
  • Ensure integrations are resilient, observable, with robust error handling and retry mechanisms.

Operational Reliability Risk Reduction

  • Ensure the platform provides accurate, timely insights for capacity risk detection and operational continuity.
  • Implement monitoring, telemetry, alerting, and runbook practices and data pipelines.

Stakeholder Engagement Influence

  • Serve as the technical bridge between Product, Operations, Engineering, and Infrastructure leadership.
  • Simplify complex technical challenges into clear, business-relevant narratives.
  • Cultivate strong partnerships with cross-functional teams to ensure alignment and adoption of estate platform.

Team Leadership Engineering Excellence

  • Proven ability to lead cross-functional engineering teams across data, backend, and web domains.
  • Experience driving architectural decisions for complex, multisystem platforms.
  • Strong communication skills with senior stakeholders, including the ability to translate technical concepts into business value.
  • Track record of influencing product strategy and delivering high-impact, user-centric solutions.
  • Provide hands-on leadership and mentorship to engineers across backend, frontend, and data domains.
  • Champion engineering best practices, code quality, architectural rigor, and CI/CD excellence.
  • Foster a culture of ownership, innovation, technical curiosity, and customer-centric product thinking.
  • Recruit, grow, and inspire high-performing technical talent.

AI/ML Automation Qualifications

  • To support the evolution of estate toward predictive and autonomous operations:

AI/ML Skills (Preferred but Highly Desirable)

  • Experience using AI to build predictive or anomalydetection models,
  • Familiarity with LLM integration, including:
    • Automating documentation and runbooks
    • Background in observability frameworks (Prometheus, Grafana, OpenTelemetry).

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