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Data Engineer (SQL, Python, Spark, Hive, Hadoop) at Unison Group

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

25 Dec, 25

Salary

0.0

Posted On

26 Sep, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, SQL, Python, Apache Spark, Hive, Hadoop, ETL, Data Quality, Data Governance, Data Security, Data Pipelines, Automation, Big Data, Cloud Platforms, Problem Solving, Communication

Industry

Business Consulting and Services

Description

Role Overview We are looking for a skilled Data Engineer with 5 years of hands-on experience in designing, developing, and optimizing big data pipelines and solutions. The ideal candidate will have strong expertise in SQL, Python, Apache Spark, Hive, and Hadoop ecosystems and will be responsible for building scalable data platforms to support business intelligence, analytics, and machine learning use cases. Key Responsibilities Design, develop, and maintain scalable ETL pipelines using Spark, Hive, and Hadoop. Write efficient SQL queries for data extraction, transformation, and analysis. Develop automation scripts and data processing workflows using Python. Optimize data pipelines for performance, reliability, and scalability. Work with structured and unstructured data from multiple sources. Ensure data quality, governance, and security throughout the data lifecycle. Collaborate with cross-functional teams (Data Scientists, Analysts, and Business stakeholders) to deliver data-driven solutions. Monitor and troubleshoot production data pipelines. Required Skills & Qualifications 5+ years of experience in Data Engineering / Big Data development. Strong expertise in SQL (query optimization, performance tuning, stored procedures). Proficiency in Python for data manipulation, scripting, and automation. Hands-on experience with Apache Spark (PySpark/Scala) for large-scale data processing. Solid knowledge of Hive for querying and managing data in Hadoop environments. Strong working knowledge of Hadoop ecosystem (HDFS, YARN, MapReduce, etc.). Experience with data pipeline orchestration tools (Airflow, Oozie, or similar) is a plus. Familiarity with cloud platforms (AWS, Azure, or GCP) is preferred. Excellent problem-solving, debugging, and communication skills.

Responsibilities

The Data Engineer will design, develop, and maintain scalable ETL pipelines using Spark, Hive, and Hadoop. They will also optimize data pipelines for performance, reliability, and scalability while ensuring data quality and security.