Chubb is seeking an experienced Data Solutions Lead to join our Global Analytics Team. This critical role will focus on designing and implementing efficient data solutions as aligned with the Business needs and DATA Roadmap, driving product improvement, operational efficiency, and enhancing data scalability across the organization. The ideal candidate will possess a deep understanding of data solutions and engineering principles, experience in developing flexible and scalable data frameworks, and a commitment to best practices in data management in support to business outcomes.

As a Data Solutions Lead, you will play a pivotal role in proposing cost effective and intuitive Data Solutions as aligned with Business needs and Global Analytics roadmap, mentoring team members, and driving innovation in DATA ecosystem. You will work closely and in alignment with B360 product owner and collaborate with cross-functional teams to deliver impactful solutions that support Chubb’s global operations, particularly in the NA region and expansion initiatives.

Key Responsibilities:

Mentorship: Work closely with, Business Users, Analysts and data engineers, mentoring them on best practices in Data Flow, Architecture, Pipelines, monitoring, testing, and data modelingStrategic Roadmap: Work Closely with B360 product owner to effectively craft solutions as aligned with Business needs and the data roadmap for assigned regions, ensuring alignment with organizational goals and efficient execution.Stakeholder Collaboration: In alignment with B360 Product Owner, Partner with stakeholders to devise solutions for projects associated with NA and 360 expansion initiatives.

Data Engineering and Automation:

Core Data Sets: Build and maintain core datasets, ensuring data quality, accessibility, and scalability in line with business priorities.Automation: Evaluate manual data processes and develop optimized automated data pipelines and workflows to streamline integration, transformation, and availability.Monitoring: Implement efficient monitoring and notification systems for data pipeline success or failure to enhance operational efficiency.Framework Development: Create a common, flexible, and scalable data framework applicable to various data projects and analytics initiatives.

Best Practices and Innovation:

Standardization: Establish standardized data components, design patterns, and methodologies to enhance project delivery speed and efficiency.Tooling Evangelist: Lead the adoption and rollout of new tools like DBT and AI applications across the organization.Innovation: Explore emerging technologies and methodologies to enhance data engineering practices, proposing and piloting innovative solutions.

Technical Excellence:

Code Reviews: Conduct code reviews and provide technical guidance to ensure solutions are standardized, maintainable, and reliable.Optimization: Assess existing data processes and workflows to identify limitations and recommend actionable solutions to improve efficiency and reduce costs.Technology Integration: Evaluate and integrate suitable technologies into existing data solutions to enhance organizational competitiveness and adaptability.Experience developing Data solutions and integrating data into Business applications.Advanced proficiency in SQL and Python.Proven experience in building robust analytics pipelines and designing effective data models.Hands-on experience with orchestration tools like Airflow.Expertise in data processing frameworks (e.g., Apache Spark, Apache Kafka) and database technologies (e.g., SQL, NoSQL).Hands-on experience with Azure, Databricks, Snowflake, and Web Scraping frameworks.Practical knowledge of Graph and Vector DB.Strong project management and communication skills, with a proven track record of driving initiatives.Ability to translate complex insights into clear, actionable narratives.Experience in communicating with stakeholders and internal customers, proactively identifying and solving their problems.

Education and Experience:

Bachelor’s degree in computer science, Information Technology, Engineering, or a related field; master’s degree preferred.Minimum of 10+ years of experience in data engineering/science, with a focus on automation and data process improvement.Minimum 5+ years of experience in leading teams.

Preferred Certifications:

Databricks Spark and Microsoft Azure certifications are a plus.Insurance background is a definite plus.