Chief Data Officer

Starr Insurance•New York, NY

2d•Onsite

About The Position

The Head of Chief Data Officer (CDO) is a transformative senior leadership role tasked with architecting, building, and scaling Starr’s global enterprise data platform, data organization, and strategy. This executive is responsible for delivering sophisticated data integration, master data management, governance, augmentation, and advanced analytics solutions tailored to support complex insurance business functions, including Actuarial, Underwriting, Claims, Compliance, and Finance—while enabling a unified, global view of enterprise-wide data assets. CDO leads the creation of a modern, comprehensive enterprise data ecosystem, championing technical innovation (cloud, medallion architecture, data augmentation, external data enrichment), robust governance, and seamless accessibility. Beyond platform leadership, the role is accountable for designing and building out a world-class data organization, encompassing core teams for architecture, engineering, analytics, governance, operations, and stewardship.

Requirements

Advanced hands-on experience: cloud-native data platforms (Databricks, Snowflake, AWS/Azure/GCP Data Services), Medallion architecture, batch/streaming pipelines, event-driven architectures, Spark, SQL, Python, Scala.
Proven success implementing MDM solutions and integrating with operational and analytical warehouses, including external data sources.
Deep experience in building ETL/ELT frameworks using diverse technologies and patterns for heterogeneous data sources.
Mastery of metadata management and data cataloging tools (Collibra, Informatica, Azure Purview).
Extensive knowledge of enterprise data governance, stewardship, lineage, residency and regulatory frameworks, audit and privacy (GDPR, CCPA, SOX, NAIC, etc.).
Demonstrated expertise with insurance data models and the process flows of core functions (Actuarial, Claims, Underwriting, Compliance, Finance).
Experience designing self-service and democratized data access environments.

Nice To Haves

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Information Systems, or related field; advanced degree highly preferred.
15+ years of progressive enterprise data and analytics experience, including 5+ years leading large-scale, global data organizations in insurance (P&C and Specialty).
Proven track record building, leading, and organizing data architecture, engineering, governance, and operations functions.
Extensive success in integrating internal and external data assets, supporting AI/ML analytics, and delivering enterprise reporting systems for insurance organizations.
Experience leading distributed (onshore/offshore/global) technical teams and managing complex vendor/partner relationships.
Deep financial management and budgeting experience for large-scale data platform/organization operations.
Outstanding leadership, transformation, and stakeholder management abilities.
Certifications in data engineering, cloud platforms, MDM, or insurance data management (e.g., AWS, Azure, GCP Architect, Databricks Certified, AID, CPCU).
Experience with core insurance, reinsurance, claims and financial platforms and their data architectures (Guidewire, Duck Creek, Majesco, SAP, Workday ERP solutions, etc.).

Responsibilities

Own and drive the global enterprise data strategy, aligning with business objectives, regulatory requirements, and future innovation.
Architect and deliver a scalable, world-class multicloud /hybrid enterprise data platform, supporting complex insurance applications and analytics.
Lead development and execution of platform onboarding, external data augmentation, and business adoption roadmaps.
Build and structure a robust data organization spanning enterprise data architecture, engineering, governance, data operations, analytics enablement, and data stewardship.
Recruit, mentor, and inspire high-performing global teams: data architects, engineers, ETL/ELT specialists, platform administrators, data governance and quality experts, data ops/operations, and metadata specialists.
Establish clear lines of responsibility, career growth paths, and collaboration practices for each data function.
Lead design and engineering of complex batch and real-time pipelines using diverse ingestion patterns (CDC, APIs, files, streaming platforms).
Implement Medallion architectures (bronze/silver/gold layers) leveraging Databricks, Fabric, and other leading cloud-based data platforms.
Architect, deliver, and manage master data management (MDM) systems for key insurance entities (policies, customers, claims, products, finance).
Develop a comprehensive data augmentation ecosystem to integrate external third-party data - e.g., catastrophe models, market feeds, regulatory databases, fraud/risk scoring, industry benchmarking - and combine/enrich internal datasets for holistic analysis and reporting.
Define, operationalize, and oversee enterprise-wide data governance policies for stewardship, quality, access, lineage, cataloging, and regulatory compliance (GDPR, CCPA, SOX, NAIC, etc.).
Ensure rigorous data privacy, usage, and retention management across all data domains.
Build teams responsible for ongoing governance operations, metadata management, and audit readiness.
Act as the chief liaison for all enterprise data initiatives, collaborating deeply with insurance business functions (Actuarial, Claims, Underwriting, Compliance, Finance, IT, and external partners).
Facilitate business requirement gathering, educate stakeholders on data capabilities, and promote adoption and value realization from data platform investments.
Govern operational reliability, performance, security, scalability, monitoring, and automation of all data assets.
Foster innovation through adoption of new technologies, best practices, and continuous improvement methods; empower agile, cross-functional working across data teams.
Champion enterprise data literacy and a culture of high performance and transformation.
Manage departmental budgets, resourcing, and vendor relationships for all data platform and organizational functions; drive value realization and cost optimization for data investments.
Proactively manage risk, business continuity, and compliance for critical data assets and operations.
Define, monitor, and optimize critical metrics: pipeline reliability and latency, data quality, master data coverage and integrity, speed to market for integrations/augmentations, platform scalability and cost efficiency, regulatory compliance, and business user satisfaction.