VP, Data Engineering

Overview

VP, Data Engineering

Location: Morristown, NJ (Hybrid, 3 days on site)

Salary: $197,000 - $275,000 + 30% Bonus

We're partnered with a growing, innovation-focused insurance organization that is investing heavily in modern data capabilities. This is a high-impact leadership role for someone who thrives at the intersection of hands-on engineering, platform modernization, and business alignment.

You'll play a critical role in transforming legacy data environments into a scalable, cloud-native ecosystem, enabling faster insights, stronger governance, and more reliable data products across the enterprise.

What You'll Do Lead Data Platform Transformation Drive migration from legacy data warehouses and datamarts to a modern AWS-based analytics platform Re-architect transformation logic into scalable, modular ELT pipelines Establish enterprise standards for data modeling aligned to core insurance domains Oversee structured cutover and decommissioning of legacy systems Build World-Class Data Engineering Practices Implement CI/CD frameworks for data pipelines, ensuring repeatable and reliable deployments Introduce DataOps best practices including automated testing, validation, and release governance Define coding standards, version control strategies, and deployment workflows Design Scalable, Reliable Data Pipelines Architect high-performance batch and incremental data pipelines Implement orchestration, monitoring, alerting, and failure recovery mechanisms Optimize performance and cost efficiency within cloud data platforms Drive Data Quality & Observability Establish robust data quality frameworks across completeness, accuracy, and timeliness Implement monitoring and observability metrics to ensure SLA adherence and system health Partner with governance teams to support lineage, auditability, and compliance Partner Across the Business Collaborate with underwriting, claims, actuarial, and finance teams to deliver trusted data products Align data models and KPIs to business needs Enable advanced analytics and modeling through strong data foundations Qualifications 10+ years of experience in data engineering, including leadership of large-scale data initiatives Proven background in the insurance industry (required) Strong expertise in Python and SQL for data engineering and pipeline development Hands-on experience building and optimizing ETL/ELT pipelines at scale Deep knowledge of AWS data services (e.g., Redshift, S3, IAM, orchestration tools) Experience migrating legacy data environments to modern cloud platforms Demonstrated success implementing CI/CD and DataOps practices

Preferred

Experience with transformation frameworks like dbt Familiarity with data quality and validation tools (e.g., Great Expectations) Infrastructure-as-Code experience Exposure to data observability platforms Understanding of regulatory and audit requirements in insurance

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