APPLICATIONS DEVELOPER

Riskdata Consulting; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Riskdata Consulting is seeking a Data Engineer to design, develop, and deploy scalable transformation frameworks across Hadoop platforms, building robust data pipelines, ingestion frameworks, and automation capabilities.

You will optimize data processing, ensure reliable integration of enterprise sources, and support data warehouse and analytics needs across banking domains, with exposure to streaming data and large-scale platforms.

Qualifications

• Design, develop, and maintain scalable data pipelines within the Enterprise Data Lake platform using Cloudera CDP, Hadoop, Spark, Hive, Java, Python, and SQL.
• Develop high-performance batch and near real-time data ingestion frameworks for onboarding enterprise source systems into the Data Lake.
• Develop complex transformation logic using Spark, HiveQL, SQL, and Java-based processing frameworks.
• Design and implement data ingestion patterns supporting structured, semi-structured, and unstructured datasets.
• Build and maintain batch and near real-time data integration solutions.
• Implement source-to-target mappings and ingestion specifications.

Responsibilities

• Develop and support solutions within the Cloudera Data Platform ecosystem.
• Build and maintain data pipelines leveraging Hadoop HDFS, Hive, Impala, Spark, Kafka, YARN, Oozie.
• Perform deployment, troubleshooting, and performance tuning of applications running on the Hadoop ecosystem.
• Partner with platform teams to ensure optimal cluster utilization and workload management.
• Ensure adherence to Data Lake onboarding standards and governance requirements.
• Investigate and resolve data quality issues and production incidents.
• Integrate data engineering solutions into CI/CD pipelines using Git, Jenkins, Azure DevOps or equivalent tools.

Tools

Cloudera CDP

Hadoop

Hive

Impala

Spark

Kafka

YARN

Oozie

HDFS

HiveQL

SQL

Java

Python

Unix/Linux scripting

Shell scripting

ETL/ELT

Data Warehousing

Data Lake

Job description

Develop and deliver scalable transformation frameworks across Hadoop platforms by building robust data pipelines, ingestion frameworks, and automation capabilities. Ensure efficient data processing, performance optimization, and reliable integration of enterprise data sources, while supporting data warehouse, enterprise analytics and downstream consumption requirements. This role offers an opportunity to work on large-scale data platforms handling structured, semi-structured, and streaming data across critical banking domains.

REQUIREMENTS

Data Engineering & Integration

• Design, develop, and maintain scalable data pipelines within the Enterprise Data Lake platform using Cloudera CDP, Hadoop, Spark, Hive, Java, Python, and SQL.
• Develop high-performance batch and near real-time data ingestion frameworks for onboarding enterprise source systems into the Data Lake.
• Develop complex transformation logic using Spark, HiveQL, SQL, and Java-based processing frameworks.
• Design and implement data ingestion patterns supporting structured, semi-structured, and unstructured datasets.
• Build and maintain batch and near real-time data integration solutions.
• Implement source-to-target mappings and ingestion specifications.

Hadoop & Cloudera Platform Development

• Develop and support solutions within the Cloudera Data Platform ecosystem.
• Build and maintain data pipelines leveraging:
  • Hadoop HDFS
  • Hive
  • Impala
  • Spark
  • Kafka
  • YARN, Oozie
• Perform deployment, troubleshooting, and performance tuning of applications running on the Hadoop ecosystem.
• Partner with platform teams to ensure optimal cluster utilization and workload management.

Data Quality & Governance

• Ensure adherence to Data Lake onboarding standards and governance requirements.
• Investigate and resolve data quality issues and production incidents.
• Ensure compliance with enterprise data governance standards.

DevOps & Platform Automation

• Integrate data engineering solutions into CI/CD pipelines using Git, Jenkins, Azure DevOps, or equivalent tools.
• Automate deployment, testing, monitoring, and operational support activities.
• Develop reusable frameworks and utilities to improve engineering productivity and platform scalability.

Production Support & Incident Management

• Provide L3 support for Data Lake application.
• Perform root cause analysis for production incidents and implement permanent fixes.
• Monitor pipeline execution, cluster health, and workload performance.
• Participate in release implementation, deployment validation, and post-production support activities.
• Strong hands on experience with Hadoop ecosystem and data pipeline orchestration.
• Hadoop/HDFS
• Hive
• Spark
• Unix/Linux scripting
• Shell scripting
• ETL/ELT development experience.
• Data modelling fundamentals.
• Performance tuning and optimization.
• Experience in Data Warehousing and Data Lake environments.
• Strong Java / Python competency with experiences.
• Strong SQL development skills.