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Solution Architect

Summary

Designs and builds end-to-end data architectures (Bronze/Silver/Gold, semantic, analytics) on AWS with Databricks or Snowflake, supporting IoT, CCTV, and AI/ML workloads for a smart-city or climate-tech initiative.

Role & Responsibilities

  • Design end-to-end data architecture covering medallion layers (Bronze/Silver/Gold), semantic layer, and business analytics layer.
  • Define data models, data contracts, governance frameworks, schema, and data warehouse design.
  • Ensure architecture supports diverse data types including IoT/telemetry, CCTV, and building system logs, and is ready for AI/ML workloads.
  • Coordinate with partners on inbound and outbound data interface frameworks and specifications.
  • Define governed data access for dashboards, APIs, and AI tools, including appropriate access controls and audit logging.
  • Assess available data to identify potential Data Science and ML use cases and inform data collection and architecture.
  • Ensure data architecture supports hypothesis testing, MLOps pipelines, and use case development.
  • Document key architectural and technical decisions, including rationale and trade-offs.
  • Work with internal teams to progressively transfer architecture and design capability.

Requirements

  • Degree in Computer Science, Engineering, or related field with 8+ years of data architecture experience.
  • Proven experience designing and building Bronze/Silver/Gold data architectures and semantic layers from the ground up, with hands-on AWS delivery using Databricks or Snowflake.
  • Experience with data contracts, governance frameworks, schema design, and automated data quality controls.
  • Experience leading data platform architecture at national or large and complex scale, including comparable international city-level or above projects beyond Singapore.
  • Experience ingesting diverse data types including IoT/telemetry, system logs, and image/video data.
  • Robotics data formats and sensor/field-equipment data streams experience is preferred.
  • Experience designing AI-ready data infrastructure for GenAI, LLM, or RAG use cases is preferred.

See also

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