VP, Forward Deployed Data Engineering

Vice President, Forward Deployed Data Engineering Job Overview Job Classification: Technology - Data Analytics & Management You will be part of the Global Technology organization, building and deploying technology solutions that directly solve real‑world business problems at scale. This role is based in our Newark, NJ office and follows a hybrid work structure: employees will be onsite at least 3 days per week. Key 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. 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. Skills & Experience 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. 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 data‑as‑a‑product concepts or enterprise data product operating models. Cloud or data platform certifications (e.g., AWS, Azure, Snowflake, Databricks). Benefits Salary range: $239,700.00 to $359,500.00 (varies by location, experience, and skills). Yearly bonus potential at every level. Medical, dental, vision, life insurance, disability insurance. Paid Time Off (PTO), parental, military, and other leave of absences. 401(k) plan with company match (up to 4%). Company‑funded pension plan. Wellness Programs including up to $1,600 per year for reimbursements of personal wellbeing items. Work/Life Resources: support for parenting, housing, senior care, finances, pets, legal matters, education, emotional and mental health, and career development. Education Benefit to finance traditional college enrollment and accredited certificate programs. Employee Stock Purchase Plan: shares purchased at 85% of the lower of two prices after one year of service. Eligibility for discretionary annual incentive program based on performance. Other benefits may not apply to part‑time employees working less than 20 hours per week. Equal Opportunity Prudential is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender identity, genetics, disability, marital status, age, veteran status, domestic partner status, medical condition or any other characteristic protected by law. If you need an accommodation to complete the application process, please email View email address on click.appcast.io. If you are experiencing a technical issue with your application or an assessment, please email View email address on click.appcast.io to request assistance. #J-18808-Ljbffr Prudential Annuities Distributors (PAD)