Data Engineer

JEET ANALYTICS PTE. LTD.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

JEET ANALYTICS PTE. LTD. is seeking a data engineering specialist to design, build and optimize large-scale data pipelines and transformation apps. You will work with Spark, PySpark, Scala and Python to ingest, transform and store data across cloud platforms, while ensuring quality, security and operational stability.

The role emphasizes real-time and batch processing with Kafka, Airflow orchestration and CI/CD automation in a collaborative enterprise environment.

Qualifications

• 6+ years of experience in Data Engineering/Big Data Engineering.
• Strong hands-on Apache Spark / PySpark
• Programming in Python and/or Scala
• Good experience with Hadoop, HDFS and Hive
• Experience developing ETL/ELT and data ingestion pipelines
• Strong SQL and data processing skills
• Experience with Azure Databricks, Azure Data Factory, AWS EMR or equivalent cloud data platforms
• Experience with Kafka / real-time streaming is an advantage
• Hands-on experience with Airflow and data pipeline orchestration
• Experience with Snowflake, Teradata, SQL Server or other enterprise databases
• Good understanding of Data Lake, Delta Lake, Data Warehousing and Data Modelling
• Experience with Git, CI/CD, Docker and automated testing

Responsibilities

• Design, develop and maintain scalable data pipelines and data ingestion frameworks for large-volume datasets.
• Develop data transformation and processing applications using Apache Spark, PySpark, Scala and Python.
• Build and optimize data pipelines using Azure Databricks, Azure Data Factory, AWS EMR and related cloud services.
• Work with Hadoop, HDFS, Hive, Snowflake, Teradata and Data Lake environments.
• Develop batch and real-time data processing solutions using Spark Structured Streaming and Kafka.
• Perform data extraction, transformation and loading across heterogeneous source and target systems.
• Develop and optimize Spark SQL, HiveQL and SQL queries for performance and cost efficiency.
• Design data models, partitioning strategies and scalable data storage architectures.
• Build and manage workflow orchestration using Apache Airflow.
• Implement CI/CD pipelines and automated testing using tools such as Jenkins, Docker, GitHub Actions and pytest.
• Troubleshoot data pipeline, performance and production issues and implement sustainable solutions.
• Collaborate with business stakeholders, architects and technology teams to understand requirements and deliver data engineering solutions.
• Ensure data quality, reliability, security and operational stability across enterprise data platforms.

Job description

Key Responsibilities

• Design, develop and maintain scalable data pipelines and data ingestion frameworks for large-volume datasets.
• Develop data transformation and processing applications using Apache Spark, PySpark, Scala and Python.
• Build and optimize data pipelines using Azure Databricks, Azure Data Factory, AWS EMR and related cloud services.
• Work with Hadoop, HDFS, Hive, Snowflake, Teradata and Data Lake environments.
• Develop batch and real-time data processing solutions using Spark Structured Streaming and Kafka.
• Perform data extraction, transformation and loading across heterogeneous source and target systems.
• Develop and optimize Spark SQL, HiveQL and SQL queries for performance and cost efficiency.
• Design data models, partitioning strategies and scalable data storage architectures.
• Build and manage workflow orchestration using Apache Airflow.
• Implement CI/CD pipelines and automated testing using tools such as Jenkins, Docker, GitHub Actions and pytest.
• Troubleshoot data pipeline, performance and production issues and implement sustainable solutions.
• Collaborate with business stakeholders, architects and technology teams to understand requirements and deliver data engineering solutions.
• Ensure data quality, reliability, security and operational stability across enterprise data platforms.

Required Skills

• 6+ years of experience in Data Engineering / Big Data Engineering.
• Strong hands‑on experience with Apache Spark / PySpark.
• Strong programming skills in Python and/or Scala.
• Good experience with Hadoop, HDFS and Hive.
• Experience developing ETL/ELT and data ingestion pipelines.
• Strong SQL and data processing skills.
• Experience with Azure Databricks, Azure Data Factory, AWS EMR or equivalent cloud data platforms.
• Experience with Kafka / real‑time streaming is an advantage.
• Hands‑on experience with Airflow and data pipeline orchestration.
• Experience with Snowflake, Teradata, SQL Server or other enterprise databases.
• Good understanding of Data Lake, Delta Lake, Data Warehousing and Data Modelling.
• Experience with Git, CI/CD, Docker and automated testing.