VP, Forward Deployed Data Engineering

Job Classification:
Technology - Data Analytics & Management

As Vice President, Forward Deployed Data Engineering within the Chief Data & AI Office, you will lead highly technical, customer‑ and business‑embedded data engineering teams responsible for delivering production‑grade data solutions directly into real operating environments across Prudential and, where applicable, external clients or partners.

This role serves as the technical bridge between business strategy, data platforms, and downstream analytics/AI capabilities, with accountability for translating complex business requirements into scalable, reliable, and governed data foundations that materially accelerate decision‑making and AI adoption.

You will operate at the intersection of forward deployed execution and enterprise data platform evolution, owning end‑to‑end delivery of embedded data initiatives while codifying reusable patterns, architectures, and accelerators that improve consistency, reliability, and enterprise time to value.

Success in this role requires deep hands‑on data engineering credibility, strong business acumen, and the ability to influence enterprise data strategy through real‑world deployment insight in complex, regulated environments.

This role is based in our office in Newark, NJ. The position is hybrid and requires your on‑site presence at least 3 days per week.

• Lead end‑to‑end forward deployed data initiatives—from technical discovery and architecture through production deployment, adoption, and operational handoff
• Embed directly with senior business partners and delivery teams to understand business workflows, data dependencies, operating constraints, and value drivers, translating them into durable data solutions
• Design and oversee delivery of enterprise‑grade batch and streaming data pipelines, including ingestion, transformation, quality validation, and publication of trusted data products
• Guide the development of analytics‑ready and AI‑ready datasets, including curated domain datasets, feature‑like data assets, and decisioning inputs
• Make and manage enterprise tradeoffs across delivery speed, cost, data quality, governance, platform fit, and long‑term maintainability
• Ensure solutions meet Prudential standards for data security, privacy, access controls, regulatory compliance, resiliency, and observability
• Establish reusable data architectures, ingestion patterns, templates, connectors, and accelerators that reduce duplication and accelerate future deployments
• Maintain tight feedback loops with enterprise data platform, architecture, and AI teams—shaping roadmaps and standards based on production realities
• Act as a senior technical escalation point during complex stakeholder engagements, delivery risks, and production issues, including hands‑on problem solving when needed
• Lead, coach, and develop managers and senior data engineers, setting clear expectations for technical rigor, business orientation, and accountability for outcomes

• Extensive experience designing, building, and operating enterprise‑scale data engineering solutions in complex environments
• Proven success leading forward‑deployed, customer‑ or business‑embedded data initiatives where solutions are delivered directly into live operations
• Strong technical judgment and credibility, with the ability to review architectures, challenge design decisions, and guide critical data engineering tradeoffs
• Demonstrated ability to translate business strategy and analytical needs into scalable data architectures and data products
• Experience operating within enterprise constraints related to data governance, security, privacy, lineage, reliability, and regulatory compliance
• Track record of influencing broader data platform direction through deployment insight, repeatable patterns, and production learnings
• Executive‑level communication skills, with the ability to translate complex data concepts into clear implications for senior business and technology leaders

• Design and operation of modern ETL/ELT pipelines using tools such as Airflow, Azure Data Factory, AWS Glue, or…