Business Systems Integration Lead

Chubb•Simsbury, CT

22h

About The Position

Chubb is on a digital‑first journey, modernizing how insurance is designed and delivered by deeply integrating technology into the business and having technology experts sit at the table with insurance experts. As a Business Integration Solutions Lead, you will sit within the business and act as a key connector between business stakeholders, data science teams, and IT. This is an individual contributor role focused on designing the integration solutions that enable models and data products to be deployed reliably and safely into production. You will work closely with IT and data engineering teams while maintaining a strong focus on business outcomes, front‑line user experience, and operational efficiency. You will leverage a strong understanding of data flows, front‑end systems, and loosely coupled, modular integration patterns to ensure models and services can be seamlessly embedded into Chubb’s digital and operational ecosystem.

Responsibilities

Design end‑to‑end integration solutions and patterns that allow data and analytical models to be deployed into production, connecting front‑end applications, decision services, and core systems in a secure, scalable, and observable way.
Define integration flows, service interfaces, events, and data contracts in partnership with IT integration and data engineering teams, with an emphasis on loosely coupled, modular, and reusable solutions.
Translate business and model deployment requirements into production‑ready integration designs, including data sourcing, invocation patterns (batch, API, event‑driven), and monitoring needs.
Ensure integrations support real‑time and near‑real‑time data needs where required, while balancing performance, resilience, and cost.
Act as the primary integration design contact for business product owners, underwriting, operations, and other domain stakeholders when introducing new models or data products into business processes.
Partner closely with IT, data engineering, and platform teams to align integration designs with enterprise integration and data architecture standards, while representing business priorities and constraints.
Collaborate with data science and analytics teams to understand model behavior, technical and data requirements, and performance characteristics, ensuring these are appropriately reflected in integration designs.
Build strong relationships with senior business leaders and technology partners, managing expectations and ensuring integration roadmaps are aligned with strategic initiatives and release plans.
Contribute to integration design standards, patterns, and reference architectures that can be reused across business lines, promoting consistency and reducing fragmentation.
Define and socialize quality criteria for integrations (e.g., reliability, latency, throughput, data quality), and ensure these are baked into designs, test plans, and acceptance criteria.
Work with data governance, enterprise and model risk, and security stakeholders to ensure integrations adhere to information security, privacy, and regulatory requirements.
Support the development of documentation—including end‑to‑end flow diagrams, data lineage views, and interface/contract specifications—that business and technology teams can easily consume.
Partner with IT teams to ensure integrations are designed with appropriate exception and log management, observability (dashboards, alerts), and operational run‑books.
Participate in solution testing (functional, integration, UAT) to validate that integrations meet business process needs and non‑functional requirements.
Support production rollout helping to triage and resolve integration issues raised by business, underwriting, compliance, or audit stakeholders.
Continuously review production metrics and feedback to identify opportunities to improve performance, resilience, and usability of integrations, and to reduce operational friction.
Serve as a subject matter expert on business data flows, integration patterns, and attribute lineage from upstream sources through to downstream consuming systems and reports.
Advise business product owners and domain leaders on integration impacts and options when scoping new models, features, or data products.
Guide and influence IT and data science teams on sound integration design principles and best practices (e.g., API vs event‑driven patterns, synchronous vs asynchronous, reuse vs bespoke).
Contribute to communities of practice or working groups focused on integration, data, and digital enablement, sharing lessons learned and emerging best practices.