Solution Engineer

Project

Build a modern cloud-native data platform that enables reliable, scalable, and secure processing of structured, semi-structured, and unstructured data. The platform supports enterprise analytics, AI, and business intelligence through high-quality data pipelines, data products, and integrations on AWS and Snowflake.

Responsibilities

• Design, build, and operate production-grade software and data solutions end-to-end, from problem definition and architecture through implementation, deployment, monitoring, and continuous improvement.
• Design and implement reliable, scalable, secure, and well-governed data pipelines and data products using AWS and Snowflake.
• Model, curate, and optimize Snowflake datasets, schemas, and data structures in line with enterprise platform standards, ensuring performance, quality, consistency, and usability for downstream consumers.
• Apply clean code, modular design, automated testing, CI/CD, observability, secure development, and maintainable architecture practices.
• Partner with business and technical stakeholders to translate requirements into robust data solutions, prioritize delivery, and identify advanced analytics and AI opportunities.
• Use AI-assisted engineering for coding, refactoring, documentation, testing, debugging, and solution exploration while maintaining engineering judgment and quality standards.
• Build cloud-native AWS integrations and automation using compute, storage, networking, security, orchestration, event-driven architectures, and managed AI services where appropriate.
• Own deployment, releases, and production operations, including troubleshooting, root-cause analysis, performance tuning, incident resolution, code reviews, pair programming, and reuse of proven engineering patterns.

Must-haves

• 7+ years of experience in software, solution, or data engineering.
• Strong hands-on AWS experience, including core services and architecture.
• Strong hands-on Snowflake experience, including data modeling, optimization, and governance.
• Proficiency in Python and/or Java.
• Experience building production-grade systems in enterprise environments.
• Knowledge of CI/CD, DevOps, Git workflows, and 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 across architecture, development, deployment, and operations.
• Strong communication and stakeholder collaboration skills.
• Upper-Intermediate English or higher.

Nice-to-haves

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

Benefits

• Remote, office-based, or hybrid work options.
• Professional, financial, and career growth opportunities.
• Opportunity to earn an additional annual bonus of up to USD 1,000 per month, depending on expertise and participation in company activities.
• Access to a corporate training portal.
• Certification compensation, including AWS and PMP certifications.
• Referral program.
• Private health insurance and compensation for sports activities.