Solution Engineer

Andersen is hiring a Solution Engineer for a project building a cloud-native data platform supporting scalable data processing, analytics, AI, and business intelligence solutions. 

The customer is a global investment management firm providing tailored investment solutions to institutional and private clients. It combines financial expertise with long-term investment strategies to help clients achieve their objectives while adapting to changing market conditions. The organization focuses on innovation, responsible investing, and operational excellence, continuously enhancing its capabilities to deliver sustainable value and support long-term growth. 

The project is focused on building a modern cloud-native data platform that enables reliable, scalable, and secure data processing across structured, semi-structured, and unstructured data sources. It involves developing high-quality data pipelines, data products, and integrations on AWS and Snowflake while supporting enterprise-wide analytics, AI, and business intelligence initiatives. 

Must-haves: 

• Experience in software / solution / data engineering for 7+ years. 
• Strong hands-on AWS experience (core services + architecture). 
• Strong hands-on Snowflake experience (data modelling, optimization, governance). 
• Proficiency in Python and/or Java. 
• Experience building production-grade systems in enterprise environments. 
• Knowledge of CI/CD, DevOps, Git workflows, Infrastructure-as-Code. 
• Strong understanding of software engineering principles and best practices. 
• Experience designing scalable data pipelines and data products. 
• Ability to work end-to-end: architecture, development, deployment, operations. 
• Strong communication and stakeholder collaboration skills. 
• Level of English – from Upper-Intermediate and above. 

Nice-to-haves: 

• AWS certifications (Solutions Architect, Data Engineer, ML Engineer). 
• Snowflake certifications (SnowPro Core or advanced). 
• Experience with AWS AI services (e.g., Amazon Bedrock). 
• Experience with RAG, agent-based AI systems, and responsible AI practices. 
• Familiarity with AI-assisted development methodologies (e.g., prompt engineering, evaluation loops). 
• Financial industry background. 
• Experience with data governance, risk, and regulatory environments.