Senior Solution Engineer

Lavu Tech Solutions Sdn Bhd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Lavu Tech Solutions Sdn Bhd in Singapore is seeking a senior software/data engineer to design, build, and operate production-grade systems end-to-end, from problem definition to deployment and monitoring.

You will design data pipelines on AWS and Snowflake, model datasets, and ensure performance, security, and governance while collaborating with business stakeholders.

The role emphasizes AI-assisted development, strong coding practices, and ownership of production operations.

Qualifications

• Bachelor's or Master's degree in a technical field.
• 7+ years delivering production-grade software or data systems.
• Strong AWS and Snowflake experience with governance.
• Proficiency in Python and/or Java and CI/CD practices.
• Experience with AI-enabled data solutions and governance.
• Excellent collaboration and communication with stakeholders.

Responsibilities

• Design, build, and operate production-grade software and data solutions end-to-end.
• Create data pipelines on AWS and Snowflake across varied data sources.
• Model and optimise Snowflake datasets, schemas, and data structures.
• Apply strong software engineering practices including testing and CI/CD.
• Partner with stakeholders to translate requirements into data solutions.
• Leverage AI-assisted engineering to accelerate development while maintaining quality.
• Own deployment, release, and production operations and incident resolution.

Skills

AWS

Snowflake

Python

Java

CI/CD

Data Engineering

Software Engineering

AI-enabled solutions

Team collaboration

Communication skills

Education

Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, AI/ML

Tools

GitHub Copilot

CI/CD tooling

Infrastructure as Code (IaC)

Serverless architectures

Data modelling & SQL tuning

Job description

You will have the following 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 across structured, semi-structured, and unstructured data sources.
• Model, curate, and optimise Snowflake datasets, schemas, and data structures in line with enterprise platform standards, ensuring performance, quality, consistency, and usability for downstream consumers.
• Apply strong software engineering practices, including clean code, modular design, automated testing, CI/CD, observability, secure development, and maintainable architecture.
• Partner with business and technical stakeholders to translate requirements into robust data solutions, prioritise delivery, and identify opportunities to enable advanced analytics and AI use cases.
• Use AI-assisted engineering as a standard part of daily development work to accelerate coding, refactoring, documentation, testing, debugging, and solution exploration while maintaining strong engineering judgement and quality standards.
• Build cloud-native integrations and automation on AWS, making effective use of services such as compute, storage, networking, security, orchestration, event-driven architectures, and managed AI services where appropriate.
• Own deployment, release, and production operations, including troubleshooting, root-cause analysis, performance tuning, incident resolution, peer code reviews, pair programming, and reuse of proven engineering patterns.

You will have the following qualifications:

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence / Machine Learning, or a related technical discipline.
• 7+ years of professional experience in a hands-on software engineering, solution engineering, or data engineering role, with a proven track record of delivering production-grade systems in enterprise environments.
• Demonstrated ability to build and operate data products, cloud services, or AI-enabled solutions with measurable business outcomes and clear operational ownership.
• Deep hands-on AWS experience is required, including practical knowledge of core services for compute, storage, networking, identity and access management, security, orchestration, monitoring, and serverless or event-driven architectures. AWS certification is preferred, ideally AWS Certified Solutions Architect - Associate, AWS Certified Data Engineer - Associate, or AWS Certified Machine Learning Engineer - Associate.
• Deep hands-on Snowflake experience is required, including data modelling, SQL performance tuning, pipeline integration, access control, cost/performance optimisation, data sharing, and platform governance. SnowPro Core Certification or advanced Snowflake certifications are a plus.
• Strong proficiency in Python and/or Java, with solid understanding of software design principles, APIs, automated testing, packaging, dependency management, and production maintainability.
• Experience with AWS AI services, including Amazon Bedrock, and familiarity with agent-based AI solution patterns, retrieval-augmented generation, model evaluation, guardrails, and responsible AI practices is preferred.
• Demonstrated habit of using AI-assisted engineering tools such as GitHub Copilot, Claude, Cursor, or similar tools as part of everyday development to improve productivity, code quality, testing, documentation, and delivery speed.
• Familiarity with harness engineering or similar AI-assisted development concepts, including structuring prompts, evaluation loops, reusable development workflows, automated checks, and feedback mechanisms to improve reliability, repeatability, and engineering quality.
• Strong hands-on engineering mindset, with a focus on code quality, sound design decisions, maintainability, and effective collaboration in team-based environments.
• Strong familiarity with the software development lifecycle, Git-based workflows, CI/CD, infrastructure-as-code concepts, automated testing, DevOps practices, and production support.
• Ability to translate ambiguous business problems into clear technical scopes, iterative delivery plans, and measurable success criteria.
• Comfortable working with sensitive and confidential data, and partnering with governance, risk, and security stakeholders to embed controls from the start.
• Strong collaboration and communication skills, with the ability to work closely with business stakeholders and cross-functional technology teams.
• Preferred: background in the financial industry, with an understanding of financial markets, data sensitivity, regulatory expectations, and enterprise risk controls.