VP, Forward Deployed Data Engineering

Job Classification:
Technology - Data Analytics & Management

Are you interested in building and deploying technology solutions that directly solve real-world business problems at scale? The Global Technology organization takes pride in delivering innovation with speed, relevance, and impact-partnering closely with our business and external stakeholders to transform how work gets done. When you join our organization at Prudential, you'll unlock an exciting and impactful career while helping shape the future of financial services through applied technology.

Your Team & Role

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. Our organization follows a hybrid work structure where employees can work remotely and from the office, as needed, based on demands of specific tasks or personal work preferences. This position is hybrid and requires your on-site presence on a reoccurring weekly basis at least 3 days per week.

What You Can Expect on a Typical Day

• 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 similar orchestration frameworks
• Strong experience with batch and streaming architectures, including Kafka, Kinesis, or cloud-native streaming services
• Data modeling and curation techniques (e.g., dimensional models, domain-oriented datasets, analytics-ready schemas)
• Hands-on experience with cloud-native data platforms such as Snowflake, Databricks, or BigQuery
• Experience operating enterprise data lakes and lakehouse architectures on AWS (e.g., S3, Redshift), Azure, or equivalent cloud platforms
• Expertise with semantic and virtualization layers (e.g., Denodo) to enable governed, reusable, and performance-optimized data access across analytical and operational use cases
• Exposure to modern data access patterns supporting analytics, AI, and real-time operational consumption
• Proficiency in Python and SQL, with experience integrating data pipelines into broader software- and API-driven ecosystems
• Familiarity with building reusable data components, connectors, and framework-level assets
• Implementation of enterprise data quality, master data management (MDM), and stewardship capabilities using platforms such as Ataccama and Informatica
• Implementation of data quality frameworks, validation checks, SLAs, monitoring, and remediation workflows
• Experience with data lineage, metadata management, access controls, and auditability across distributed data environments
• Strong understanding of enterprise data risk, privacy, and compliance requirements CI/CD practices for data pipelines and data infrastructure, including version control, automated testing, and automated deployment
• Operational readiness, monitoring, alerting, and incident response for business-critical data systems

• Experience in financial services, insurance, asset management, or other highly regulated industries
• Experience working closely with analytics, data science, or AI engineering teams to enable downstream consumption
• Familiarity with data-as-a-product concepts or enterprise data product operating models
• Cloud or data platform certifications (e.g., AWS, Azure, Snowflake, Databricks)

• Market competitive base salaries, with a yearly bonus potential at every level.
• Medical, dental, vision, life insurance, disability insurance, Paid Time Off (PTO), and leave of absences, such as parental and military leave.
• 401(k) plan with company match (up to 4%).
• Company-funded pension plan.
• Wellness Programsincluding up to $1,600 a year for reimbursement of items purchased to support personal wellbeing needs.
• Work/Life Resources to help support topics such as parenting, housing, senior care, finances, pets, legal matters, education, emotional and mental health, and career development.
• Education Benefit to help finance traditional college enrollment toward obtaining an approved degree and many accredited certificate programs.
• Employee Stock Purchase Plan: Shares can be purchased at 85% of the lower of two prices (Beginning or End of the purchase period), after one year of service.