jobs in RiDiK (a Subsidiary Of CLPS. Nasdaq: CLPS)

Kerja Sepenuh Masa, Senior Data Architect di RiDiK (a Subsidiary Of CLPS. Nasdaq: CLPS) - Maukerja

Senior Data Architect

RiDiK (a Subsidiary Of CLPS. Nasdaq: CLPS)

Undisclosed

Sepenuh Masa

Singapore

Kongsi
Simpan

Lokasi Kerja

  • Singapore

Penerangan Kerja

Tanggungjawab

Role: Data Integration Architect

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

Location : Singapore

Notice Period: Immediate to 15days



Key Responsibilitie

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


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


  • ty
    3. Core Responsibilit
  • ies Data Integration Des
  • ign• Design data ingestion pipelines across multiple syst
  • ems• Integrate data fr
  • om:o Source systems → Data platforms (e.g., Snowflake, Databric
  • ks)• Define architecture for data movement and f
  • low Cross-System Integrat
  • ion• Enable integration betwe
  • en:o Source systems (e.g., S
  • AP)o Data platforms (e.g., Snowflake, Databric
  • ks)o Consumption layers (e.g., Data Science tools like SageMak
  • er) Architecture Owners
  • hip• Design solutions wi
  • th:o Clear logic and ration
  • aleo Optimized data movement (avoid unnecessary large loads like TB-scale pul
  • ls)• Provide guidance to engineering te
  • ams Hands-on / Support R
  • ole• Shou
  • ld:o Assist in build/implementation if requi
  • redo Monitor pipelines and integrati


  • ons
    4. Required Technical Sk
  • ills Must-
  • Have• AWS (mandatory) – customer is moving to
  • AWS• Strong experience
  • in:o Data integration architec
  • tureo Pipeline design (ETL/ELT conce
  • pts)o Handling large-scale data sys
  • tems Good-to-
  • Have• API integrations (REST API / Open
  • API)• SAP integration experi
  • ence• Cross-cloud experi
  • enceo AWS ↔ Azure / other cloud integrat
  • ions• Exposure
  • to:o Snowflake / Databr
  • ickso Data Science platforms (SageMaker, DataRobot, e
  • tc.)• Understanding
  • of:o Monito
  • ringo Cost optimiza
  • tiono Compute/resource u


  • sage
    5. Scope Clarifica
  • tions• Focus Area: Data ingestion & integr
  • ation• Not manda
  • tory:o Deep data processing within a single pla
  • tform• Emphasi
  • s on:o Moving and connecting data across ecosy
  • stemso Enabling downstream consumption (analytics/data sci


ence)

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