Senior Solution Engineer (with Python OR Java + Snowflake-mandatory)_BGC_Hybrid_up to 200k

We are hiring for Senior Solution Engineer (with Python OR Java + snowflake) in BGC, Taguig City.

This is a Hybrid work setup with 3 days RTO -Mon and Fri as default + 1 day of your choice (Midshift) and 2 days WFH. Salary is up to 200k based on level.

Requirements:

• 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.

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.