Senior Data Platform Architect: IoT, AI/ML Ready

Epergne Solutions; Singapore; On-site; SGD 150,000 - 230,000; Full time

Job summary

Epergne Solutions is seeking a Senior Solution Architect (Data Platform) in Singapore to drive end-to-end data architecture across medallion layers, semantic models, and analytics platforms.

You will define data models, governance, and deployment patterns for scalable data warehouses and lakehouses, while collaborating across teams on AWS, Databricks, and Snowflake implementations, including MLOps readiness.

Qualifications

• Bachelor’s degree in CS, Engineering, or related field.
• 8+ years experience in data/solution architecture.
• Strong experience with medallion data architectures, semantic layers, and data warehouses/lakehouses.
• Hands-on with AWS and platforms like Databricks or Snowflake.
• Experience in data governance, schema design, and automated data quality controls.

Responsibilities

• Design end-to-end data architecture including bronze/silver/gold layers, semantic models, and analytics layers.
• Define data models, contracts, governance frameworks, and schema standards.
• Lead data warehouse and lakehouse architecture design decisions.
• Design scalable data ingestion frameworks for IoT, telemetry, logs, and media data.
• Enable secure data access for dashboards, APIs, analytics, and AI/ML apps.
• Support MLOps and GenAI-readiness with scalable architecture.
• Collaborate with tech teams on interfaces and architecture docs.
• Document key decisions, trade-offs, and implementation guidelines.

Skills

Data architecture

Solution architecture

Medallion architecture

Semantic layers

Data governance

AI/ML-ready data

Education

Bachelor’s degree in Computer Science/Engineering

Tools

Databricks

Snowflake

AWS

Job description

Epergne Solutions is seeking a Senior Solution Architect (Data Platform) in Singapore to drive end-to-end data architecture across medallion layers, semantic models, and analytics platforms.

You will define data models, governance, and deployment patterns for scalable data warehouses and lakehouses, while collaborating across teams on AWS, Databricks, and Snowflake implementations, including MLOps readiness.