Job
Description
The Solution Architect Data Engineer will design, implement, and manage data solutions for the insurance business, leveraging expertise in Cognos, DB2, Azure Databricks, ETL processes, and SQL. The role involves working with cross-functional teams to design scalable data architectures and enable advanced analytics and reporting, supporting the company's finance, underwriting, claims, and customer service operations. Key Responsibilities: Data Architecture & Design: Design and implement robust, scalable data architectures and solutions in the insurance domain using Azure Databricks, DB2, and other data platforms. Data Integration & ETL Processes: Lead the development and optimization of ETL pipelines to extract, transform, and load data from multiple sources, ensuring data integrity and performance. Cognos Reporting: Oversee the design and maintenance of Cognos reporting systems, developing custom reports and dashboards to support business users in finance, claims, underwriting, and operations. Data Engineering: Design, build, and maintain data models, data pipelines, and databases to enable business intelligence and advanced analytics across the organization. Cloud Infrastructure: Develop and manage data solutions on Azure, including Databricks for data processing, ensuring seamless integration with existing systems (e.g., DB2, legacy platforms). SQL Development: Write and optimize complex SQL queries for data extraction, manipulation, and reporting purposes, with a focus on performance and scalability. Data Governance & Quality: Ensure data quality, consistency, and governance across all data solutions, implementing best practices and adhering to industry standards (e.g., GDPR, insurance regulations). Collaboration: Work closely with business stakeholders, data scientists, and analysts to understand business needs and translate them into technical solutions that drive actionable insights. Solution Architecture: Provide architectural leadership in designing data platforms, ensuring that solutions meet business requirements, are cost-effective, and can scale for future growth. Performance Optimization: Continuously monitor and tune the performance of databases, ETL processes, and reporting tools to meet service level agreements (SLAs). Documentation: Create and maintain comprehensive technical documentation including architecture diagrams, ETL process flows, and data dictionaries. Required Qualifications: Bachelors or Masters degree in Computer Science, Information Systems, or a related field. Proven experience as a Solution Architect or Data Engineer in the insurance industry, with a strong focus on data solutions. Hands-on experience with Cognos (for reporting and dashboarding) and DB2 (for database management). Proficiency in Azure Databricks for data processing, machine learning, and real-time analytics. Extensive experience in ETL development, data integration, and data transformation processes. Strong knowledge of Python, SQL (advanced query writing, optimization, and troubleshooting). Experience with cloud platforms (Azure preferred) and hybrid data environments (on-premises and cloud). Familiarity with data governance and regulatory requirements in the insurance industry (e.g., Solvency II, IFRS 17). Strong problem-solving skills, with the ability to troubleshoot and resolve complex technical issues related to data architecture and performance. Excellent verbal and written communication skills, with the ability to work effectively with both technical and non-technical stakeholders. Preferred Qualifications: Experience with other cloud-based data platforms (e.g., Azure Data Lake, Azure Synapse, AWS Redshift). Knowledge of machine learning workflows, leveraging Databricks for model training and deployment. Familiarity with insurance-specific data models and their use in finance, claims, and underwriting operations. Certifications in Azure Databricks, Microsoft Azure, DB2, or related technologies. Knowledge of additional reporting tools (e.g., Power BI, Tableau) is a plus. Key Competencies: Technical Leadership: Ability to guide and mentor development teams in implementing best practices for data architecture and engineering. Analytical Skills: Strong analytical and problem-solving skills, with a focus on optimizing data systems for performance and scalability. Collaborative Mindset: Ability to work effectively in a cross-functional team, communicating complex technical solutions in simple terms to business stakeholders. Attention to Detail: Meticulous attention to detail, ensuring high-quality data output and system performance.
