全职 Data Integration Manager 工作, 薪水, RiDiK (a Subsidiary Of CLPS. Nasdaq: CLPS) 公司招聘中 - Ricebowl

Role: Data Integration Architect

Exp: 10-15yrs( Not more than that)

Location : Singapore

Notice Period: Immediate to 15days

Key Responsibilities

• . Architecture Experience:
• o Minimum 1.5 to 2 years in Data Architect (or similar) role
• o Candidates transitioning from other roles are acceptable
• o Should not be a complete beginner in architecture (e.g., <1 year not suitable)

• • Ideal profile:
• o “Emerging architect” (subset of enterprise architect)
• o Hands-on + design capability

• 3. Core Responsibilities
• Data Integration Design
• • Design data ingestion pipelines across multiple systems
• • Integrate data from:
• o Source systems → Data platforms (e.g., Snowflake, Databricks)
• • Define architecture for data movement and flow
• Cross-System Integration
• • Enable integration between:
• o Source systems (e.g., SAP)
• o Data platforms (e.g., Snowflake, Databricks)
• o Consumption layers (e.g., Data Science tools like SageMaker)
• Architecture Ownership
• • Design solutions with:
• o Clear logic and rationale
• o Optimized data movement (avoid unnecessary large loads like TB-scale pulls)
• • Provide guidance to engineering teams
• Hands-on / Support Role
• • Should:
• o Assist in build/implementation if required
• o Monitor pipelines and integrations

• 4. Required Technical Skills
• Must-Have
• • AWS (mandatory) – customer is moving to AWS
• • Strong experience in:
• o Data integration architecture
• o Pipeline design (ETL/ELT concepts)
• o Handling large-scale data systems
• Good-to-Have
• • API integrations (REST API / Open API)
• • SAP integration experience
• • Cross-cloud experience
• o AWS ↔ Azure / other cloud integrations
• • Exposure to:
• o Snowflake / Databricks
• o Data Science platforms (SageMaker, DataRobot, etc.)
• • Understanding of:
• o Monitoring
• o Cost optimization
• o Compute/resource usage

• 5. Scope Clarifications
• • Focus Area: Data ingestion & integration
• • Not mandatory:
• o Deep data processing within a single platform
• • Emphasis on:
• o Moving and connecting data across ecosystems
• o Enabling downstream consumption (analytics/data science)