Solutions Designer – AWS Data Engineering & Architecture

Required Qualifications & Skills 5+ years of relevant professional experience in solutions design, data engineering, data architecture, or related roles. Strong hands-on experience with AWS data services, particularly: Amazon S3 Amazon Redshift Amazon Athena AWS Glue AWS Lambda Proven hands-on expertise in PySpark for building and optimizing scalable batch and streaming data pipelines. Experience designing and implementing large-scale Data Lakehouse and Data Warehouse architectures. Knowledge of Master Data Management (MDM), Advanced Analytics platforms, and data API architecture. Strong understanding of data governance, data quality, security, and metadata management principles. Experience with API-based integration and enterprise data integration patterns. Strong analytical, problem-solving, and solution-design capabilities. Excellent stakeholder management and communication skills, with the ability to bridge business and technical requirements. Experience working with geographically distributed, cross-country, and cross-functional teams. Preferred Qualifications Experience in the insurance domain , including familiarity with insurance business processes and data environments. Relevant AWS certifications in data engineering, cloud architecture, or analytics. Experience with enterprise-scale data modernization, cloud migration, or data platform transformation initiatives. Familiarity with data security, access controls, regulatory compliance, and enterprise data architecture standards. Key Responsibilities Design and architect scalable, secure, and high-performance data solutions using AWS cloud services. Develop and optimize data processing pipelines using PySpark for large-scale batch and streaming workloads. Design and implement enterprise data platforms, including Data Lakehouse, Data Warehouse, MDM, Advanced Analytics, and Data API solutions. Leverage AWS services such as Amazon S3, Redshift, Athena, Glue, and Lambda to build robust data architectures. Define data architecture standards, integration patterns, and best practices for performance, scalability, reliability, and security. Establish and support data governance, data quality, metadata management, and MDM frameworks. Design API-based integration solutions to enable seamless and secure data exchange across systems. Engage with business stakeholders, technical teams, and enterprise architects to gather requirements and translate business needs into effective technical designs. Collaborate with cross-country and cross-functional teams to ensure alignment with business objectives and enterprise architecture standards. Identify opportunities to improve data processing efficiency, platform performance, and overall solution quality.