Senior Data Engineer: Databricks AI Pipelines

TOSS-EX PR PTE. LTD.

Singapore

On-site

SGD 120,000 - 180,000

Full time

14 days+

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Job summary

Koantek is seeking a Senior Data Engineer to design and build robust data pipelines, integrate structured and unstructured data, and develop tools for processing and analysis. You will manage data infrastructure, optimize workflows, and guide data-driven strategies while collaborating with data scientists and stakeholders to deliver scalable, production-ready solutions.

Responsibilities include implementing large-scale data transformations, ensuring CI/CD and DataOps practices, and applying

Qualifications

  • Bachelor’s degree in CS, IT, or related field.
  • 6+ years as a Data Engineer with multi-cloud experience (AWS/Azure/GCP).
  • Proven Databricks experience for large-scale data processing.
  • Experience building streaming and batch pipelines; DataOps & CI/CD.
  • Strong Spark/Scala, Python, and PySpark proficiency.

Responsibilities

  • Design and build robust data pipelines and data infrastructure.
  • Integrate structured and unstructured data from multiple sources.
  • Develop tools for data processing, analysis, and reporting.
  • Guide data-driven strategies and coordinate with data scientists.
  • Apply Databricks best practices across cloud environments.

Skills

Spark/Scala
Python
PySpark
Data Modeling
Streaming
Spark SQL
Kafka
StreamSets
ML Frameworks
TensorFlow
PyTorch
CI/CD in Data

Education

Bachelor's degree in Computer Science / IT

Tools

Databricks
AWS
Azure
GCP
MapReduce

Job description

Koantek is seeking a Senior Data Engineer to design and build robust data pipelines, integrate structured and unstructured data, and develop tools for processing and analysis. You will manage data infrastructure, optimize workflows, and guide data-driven strategies while collaborating with data scientists and stakeholders to deliver scalable, production-ready solutions.

Responsibilities include implementing large-scale data transformations, ensuring CI/CD and DataOps practices, and applying

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