Description

Lead Data Architect who will define and drive the data and AI architecture strategy for a Workplace Solutions division within the insurance domain. This role leads the design of modern cloud-based data platforms, machine learning operations, and governance frameworks that support enterprise analytics, intelligent automation, and AI innovation. Day to day, this person will create architecture roadmaps, guide Agile Release Trains (ARTs), partner with business leaders, and operationalize scalable, secure, and ethical data ecosystems.

Top 4 Skills

• Enterprise Data Architecture (cloud-native, AWS preferred)
• Distributed data systems (warehouse / lakehouse)
• MLOps & ML lifecycle management (SageMaker, MLflow)
• Python, SQL, Snowflake, MongoDB

Responsibilities

Data & AI Architecture Leadership

• Design and evolve enterprise-scale data platforms and AI architecture
• Enable analytics, ML, and intelligent automation across the division
• Architect distributed data systems in microservices and event-driven environments

Strategy & Roadmapping

• Define data modernization and AI strategy aligned to business goals
• Develop long-term roadmaps for MLOps, governance, and AI enablement
• Translate business strategies into measurable data solutions

AI & ML Enablement

• Lead AI prototypes, POCs, and production of ML pipelines
• Operationalize models using containers, APIs, and serverless frameworks
• Establish CI/CD for ML and model observability practices

Data Engineering & Modeling Standards

• Oversee ETL/ELT architecture from source to target
• Define logical and physical data modeling standards (dimensional, normalized, denormalized)
• Support BI, analytics, and AI data access patterns

Governance & Responsible AI

• Establish data quality, lineage, and trust frameworks
• Promote ethical AI, explainability, and regulatory compliance
• Implement observability and governance best practices

Leadership & Influence

• Guide architecture across Agile Release Trains (ARTs)
• Influence cross-functional stakeholders and enterprise architects
• Mentor engineers and data professionals

Qualifications

Required:

• 5+ years leading enterprise data architecture initiatives
• Expertise in distributed data systems and lakehouse/warehouse architecture
• Experience with ML operationalization frameworks (MLflow, SageMaker, similar)
• Strong Python, SQL, Snowflake, MongoDB skills
• AWS cloud experience (Glue, Redshift, SageMaker, Bedrock preferred)
• ETL/ELT design and implementation experience
• Strong logical and physical data modeling expertise
• Experience in microservices and event-driven architecture
• Knowledge of data governance, explainability, and responsible AI
• Familiarity with CI/CD for ML, data observability, and lineage tools
• TOGAF Certification
• Demonstrated strategic vision, roadmapping, and executive influence

Nice to Have:

• Group Benefits or insurance industry experience
• Kafka, Spark, real-time analytics experience
• Data mesh familiarity
• Direct AI development experience

Key Skills

Education

Any Graduate