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.