Senior Solution Engineer

Snowflake Snowflake · Data AI · Singapore · Solution Engineering

Senior Solution Engineer for Snowflake's enterprise customers in Singapore, focusing on Financial Services and Telecommunications. The role involves driving adoption, designing solutions, leading POCs, and acting as a technical advisor to senior stakeholders, guiding them on using Snowflake for data, analytics, and AI strategies. This is a field-facing role requiring deep technical skills, industry context, and the ability to communicate complex technical capabilities into business value.

What you'd actually do

  1. Own the technical expansion strategy for a portfolio of Snowflake customers in Singapore.
  2. Design and deliver compelling demos and technical narratives tailored to each customer’s architecture and use cases.
  3. Lead and execute POCs/Pilots that prove value and de‑risk platform decisions, including success criteria definition and joint execution plans.
  4. Translate complex technical capabilities into clear business value and ROI for senior non-technical audiences.
  5. Collaborate closely with Sales, Customer Success, Professional Services, Product, and Partners to ensure successful customer outcomes.

Skills

Required

  • Extensive 10 years working experience in customer facing solution engineering role
  • Strong competency on modern data stacks, ETL/ELT, data warehouse/lake, data science , Gen AIand Data Apps
  • Excellent ability to present to both technical and executive audiences, whether through impromptu whiteboarding or structured presentations and demos.
  • Hands-on expertise with SQL & Spark

Nice to have

  • Experience with large-scale cloud data and ai platforms
  • Programming/scripting experience (e.g., Python, Java, Spark)
  • Experience selling or supporting enterprise data/analytics platforms
  • Strong stakeholder management and value-based selling skills
  • Degree in Computer Science, Engineering, Mathematics, or related field skills
  • Hands-on experience with Lakehouse architectures
  • Exposure to Data Science / AI / ML / Generative AI workloads
  • Familiarity with BI tools (e.g., Tableau, Power BI)
  • Cloud platform experience: AWS, Azure, or GCP
  • Relevant services (e.g., S3, Glue, SageMaker, Azure Data Factory, etc.)
  • Experience with OLAP data modeling and enterprise data architecture
  • Background in Financial Services (either as a practitioner or vendor)
  • Experience selling enterprise SaaS solutions

What the JD emphasized

  • AI Data Cloud
  • Gen AI
  • AI strategy
  • AI workloads