VP, Forward Deployed Data Engineering

Job Classification:

Are you interested in building and deploying technology solutions that directly solve realworld business problems at scale The Global Technology organization takes pride in delivering innovation with speed relevance and impactpartnering closely with our business and external stakeholders to transform how work gets done. When you join our organization at Prudential youll 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 businessembedded data engineering teams responsible for delivering productiongrade 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 decisionmaking and AI adoption.

You will operate at the intersection of forward deployed execution and enterprise data platform evolution owning endtoend 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 handson data engineering credibility strong business acumen and the ability to influence enterprise data strategy through realworld deployment insight in complex regulated environments.

This role is based in our office in Newark NJ. Our organization follows a hybrid work structure whereemployees 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 endtoend forward deployed data initiativesfrom 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 enterprisegrade batch and streaming data pipelines including ingestion transformation quality validation and publication of trusted data products
• Guide the development of analyticsready and AIready datasets including curated domain datasets featurelike data assets and decisioning inputs
• Make and manage enterprise tradeoffs across delivery speed cost data quality governance platform fit and longterm 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 teamsshaping roadmaps and standards based on production realities
• Act as a senior technical escalation point during complex stakeholder engagements delivery risks and production issues including handson 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

Skills & Experience You Bring

• Extensive experience designing building and operating enterprisescale data engineering solutions in complex environments
• Proven success leading forwarddeployed customer or businessembedded 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
• Executivelevel communication skills with the ability to translate complex data concepts into clear implications for senior business and technology leaders

Technical Expertise

• 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 cloudnative streaming services
• Data modeling and curation techniques (e.g. dimensional models domainoriented datasets analyticsready schemas)
• Handson experience with cloudnative 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 performanceoptimized data access across analytical and operational use cases
• Exposure to modern data access patterns supporting analytics AI and realtime operational consumption
• Proficiency in Python and SQL with experience integrating data pipelines into broader software and APIdriven ecosystems
• Familiarity with building reusable data components connectors and frameworklevel 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 businesscritical data systems

Preferred Qualifications

• 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 dataasaproduct concepts or enterprise data product operating models
• Cloud or data platform certifications (e.g. AWS Azure Snowflake Databricks)

#LI-Hybrid

• 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 Programs including up to $1600 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.

Eligibility to participate in a discretionary annual incentive program is subject to the rules governing the program whereby an award if any depends on various factors including without limitation individual and organizational performance. To find out more about our Total Rewards package visit Work Life Balance Prudential Careers. Some of the above benefits may not apply to part-time employees scheduled to work less than 20 hours per week.