Senior Data Engineer – Scalable Pipelines for AI & GenAI

MORGAN MCKINLEY PTE. LTD.

Singapore

On-site

SGD 110,000 - 160,000

Full time

38 hours ago
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Job summary

Morgan McKinley Pte Ltd is seeking a Senior Data Engineer to architect, develop, and refine high-throughput data systems powering analytics, AI, and RAG solutions.

You will build scalable ingestion/processing across hybrid cloud, optimise batch/streaming workflows with Databricks and Kafka, and deliver secure data for reporting, ML, and GenAI use cases. Strong Python/SQL and Spark experience required.

Qualifications

  • Bachelor's degree in CS, engineering, or related field.
  • 5+ years in data engineering or cloud-scale analytics with production pipelines.
  • Experience with Python and SQL for data transformation and validation.
  • Hands-on with Spark (PySpark) and large-scale processing.
  • Experience with knowledge bases and RAG for AI use cases.
  • Strong problem-solving and independent work ethic.

Responsibilities

  • Build scalable ingestion, transformation, and serving across hybrid cloud environments.
  • Optimise batch and streaming workflows with Databricks and Kafka.
  • Deliver secure data for reporting, ML, and GenAI use cases.
  • Lead setup of knowledge bases and RAG stacks for AI apps.
  • Ensure pipeline reliability, observability, and production readiness.
  • Drive CI/CD, version control, and data governance.

Skills

Python
SQL
Apache Spark
Data pipelines
Databricks
Kafka
RAG
CI/CD

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Databricks
Kafka
Delta Lake
Microsoft Fabric

Job description

Morgan McKinley Pte Ltd is seeking a Senior Data Engineer to architect, develop, and refine high-throughput data systems powering analytics, AI, and RAG solutions.

You will build scalable ingestion/processing across hybrid cloud, optimise batch/streaming workflows with Databricks and Kafka, and deliver secure data for reporting, ML, and GenAI use cases. Strong Python/SQL and Spark experience required.

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