Data Architect - Azure - Insurance

Senior Data Architect (Azure)

Insurance / ReInsurance

Location: London

Hybrid:3 Days a week

Position Overview: As a Senior Data Architect, you will be responsible for designing, implementing, and optimising our data architecture / data lakehouse to support cloud-based ETL processes and digital transformation projects.

Key Responsibilities:

• Design and develop scalable data architectures for cloud-based environments.
• Lead the implementation of ETL processes to ensure efficient data extraction, transformation, and loading.
• Collaborate with stakeholders to understand business requirements and translate them into technical solutions.
• Ensure data quality, integrity, and security across all data platforms.
• Optimize data storage and retrieval processes to enhance performance and scalability.
• Provide technical guidance and mentorship to junior data engineers and developers.
• Stay up-to-date with emerging technologies and industry trends to drive continuous improvement.
• Develop and maintain documentation for data architecture, processes, and best practices.

Key Requirements:

• Experienced in Data Architecture, Data Modelling, and Data Management.
• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
• Proven experience as a Data Architect, with a focus on cloud-based environments (primarily Azure), Azure Fabric, Data Factory, Data Lakes, Data Bricks, and ETL processes.
• Experience with database management systems (e.g., MS SQL, Cosmos DB, Mongo DB, PostgreSQL) along with strong DML and DQL experience.
• Strong knowledge and experience in Bulk and Event-Driven Data Analytics.
• Experience with Data Catalogue and Meta Data Governance tools like Collibra.
• Familiarity with data modelling, data governance, and data security best practices.
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
• Strong communication and interpersonal skills.

Preferred Skills:

• Experience with big data technologies (e.g., Hadoop, Spark).
• Knowledge of machine learning and AI integration with data architectures.
• Certification in cloud platforms or data management.