Applications Developer

REGTECH INSIGHT PTE. LTD.; Singapore; Presencial; SGD 120.000 - 180.000; Jornada completa

Descripción de la vacante

REGTECH INSIGHT PTE. LTD. is seeking a skilled Data Engineer to design and deliver scalable data pipelines across Hadoop platforms, enabling enterprise data lakes, analytics, and downstream consumption. You will handle structured, semi-structured, and streaming data within critical banking domains.

The role emphasizes batch and near real-time ingestion, data quality, governance, and automation via CI/CD, Git, Jenkins, and Azure DevOps, ensuring reliable integration of diverse data sources.

Formación

• Design, develop, and maintain scalable data pipelines for Enterprise Data Lake.
• Develop high-performance batch and near real-time data ingestion frameworks.
• Implement data ingestion patterns for structured, semi-structured, and unstructured data.
• Ensure governance and quality in data onboarding and production systems.

Responsabilidades

• Design and maintain scalable data pipelines using CDP, Hadoop, Spark, Hive, Java, Python, and SQL.
• Develop real-time data ingestion frameworks for onboarding enterprise sources.
• Build data transformations with Spark, HiveQL, SQL, and Java-based processing.
• Automate deployment, testing, and monitoring within CI/CD pipelines.
• Provide L3 production support, troubleshoot incidents, and optimize performance.

Conocimientos

Data pipelines

Hadoop

Spark

Hive

Java

Python

SQL

CI/CD

Git

Jenkins

Azure DevOps

Unix/Linux scripting

ETL/ELT

Data Warehousing

Data Lake

Performance tuning

Data governance

Incident management

Kafka

Herramientas

Cloudera CDP

Hadoop

Hive

Impala

Spark

Kafka

YARN

Oozie

Descripción del empleo

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.