Director of Cloud Data Architecture - Hybrid

Dir Data Architecture - GT06AE

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The Hartford's Enterprise Data Services (EDS) is seeking a Director of Cloud Data Architecture responsible for modernizing and simplifying a portfolio of assets supporting Commercial Lines and Sales & Distribution portfolio.

This role will have a Hybrid work arrangement, with the expectation of working in an office location (Hartford, CT and Charlotte, NC) 3 days a week (Tuesday through Thursday).

Responsibilities:

Data Architecture Strategy:

• As a leader, you’ll be responsible for designing and delivering the Data Architecture strategy for Commercial Lines and Sales & Distribution.
• You’ll collaborate directly with senior stakeholders across various functional areas.

Current and Target State Architectures:

• Develop and maintain both current and target state data architectures.
• Define strategy and roadmaps to guide transformation efforts.

Data Transformation Roadmap:

• Create a roadmap for Data Transformation, addressing challenges related to legacy technology stacks, complex data pipelines, data freshness, delivery speed, and quality.
• This involves modernizing and simplifying existing data assets.

Architecture Patterns:

• Create architecture patterns for both Hybrid and Cloud Data ecosystems (including AWS Cloud, Snowflake, and On-Premises).
• Ensure alignment with data architecture principles, standards, strategies, and target states.
• Consider data consumption across the entire ecosystem.

Industry Trends and Implications:

• Analyze emerging business and technology trends (such as Gen AI).
• Translate these trends into actionable capabilities within functional and technology domains.
• Researches and evaluates alternative solutions and recommends the most efficient and cost-effective solution for the systems design.

Real-Time Data Integration:

• Design architecture patterns using near real-time or real-time data cache for integration with operational systems.

Governance and Best Practices:

• Define and enforce data architecture standards, procedures, metrics, and policies.
• Be a thought leader, driving positive change and simplification while improving delivery speed.
• Mature data architecture processes, promote best practices, and establish reference architectures.
• Leader who is part of the Enterprise Data Services Architecture Center of Excellence driving architecture standards, best practices and governance processes to ensure projects are compliant with overall data strategy.

Large-Scale Data Ecosystems:

• Architect and design large-scale Data Ecosystems, leveraging modern data and analytical tools.
• Leverage concepts like Data Domains and Data Products.

Leadership and Collaboration:

• Lead initiatives for continuous improvement, ensuring alignment between business strategies and technology roadmaps.
• Provide technical leadership, mentoring, and coaching to technical teams.
• Collaborate with extended architecture, business, and IT support teams to develop functional and technical blueprints.
• Possesses functional knowledge and skills reflective of a competent practitioner with the ability to deliver on work of highest technical complexity.
• Enables a high performing team that has a culture of continuous learning, collaboration, and is focused on business outcomes.
• Educate and influence multiple stakeholders.

Automation and Scalability:

• Ensure that automation and data strategy implementations are scalable, reusable, and integrated.

Qualifications:

• 10+ years of experience in Architecture, design and development of large-scale data ecosystems
• Mastery level Data Architecture skills – a deep understanding of data architecture patterns data warehouse, integration, data lake, data domains, data products and cloud technology capabilities
• Strong experience with design and development of complex data ecosystems leveraging next generation of cloud technology stack across AWS Cloud, PySpark and Snowflake.
• In depth knowledge of Data management strategies, principles and practices including experience with framework for data quality, data governance, data domains,