VP, Forward Deployed Data Engineering

About the role

The Global Technology organization at Prudential takes pride in delivering innovative technology solutions that directly address real-world business challenges. As Vice President, Forward Deployed Data Engineering within the Chief Data & AI Office, you will lead data engineering teams responsible for delivering production-grade data solutions directly into real operating environments.

Responsibilities

• 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
• 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

Requirements

You bring 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, strong technical judgment and credibility, 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, and proficiency in Python and SQL, with experience integrating data pipelines into broader software- and API-driven ecosystems.

Qualifications

• 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
• Technical expertise in 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
• 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
• Experience with building reusable data components, connectors, and framework-level assets
• Experience implementing enterprise data quality, master data management (MDM), and stewardship capabilities using platforms such as Ataccama and Informatica
• Experience implementing data quality frameworks, validation checks, SLAs, monitoring, and remediation workflows
• Strong understanding of enterprise data risk, privacy, and compliance requirements
• Experience with 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

Skills

• 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
• Technical expertise in 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
• 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
• Experience with building reusable data components, connectors, and framework-level assets
• Experience implementing enterprise data quality, master data management (MDM), and stewardship capabilities using platforms such as Ataccama and Informatica
• Experience implementing data quality frameworks, validation checks, SLAs, monitoring, and remediation workflows
• Strong understanding of enterprise data risk, privacy, and compliance requirements
• Experience with 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

Benefits

Specific pricing for the role may vary within the above range based on many factors including geographic location, candidate experience, and skills. 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 $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. 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.

Pay

$239,700.00 to $359,500.00

Schedule

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.